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Structural Positional Encoding for knowledge integration in transformer-based medical process monitoring (2403.08836v1)
Published 13 Mar 2024 in cs.LG and cs.AI
Abstract: Predictive process monitoring is a process mining task aimed at forecasting information about a running process trace, such as the most correct next activity to be executed. In medical domains, predictive process monitoring can provide valuable decision support in atypical and nontrivial situations. Decision support and quality assessment in medicine cannot ignore domain knowledge, in order to be grounded on all the available information (which is not limited to data) and to be really acceptable by end users. In this paper, we propose a predictive process monitoring approach relying on the use of a {\em transformer}, a deep learning architecture based on the attention mechanism. A major contribution of our work lies in the incorporation of ontological domain-specific knowledge, carried out through a graph positional encoding technique. The paper presents and discusses the encouraging experimental result we are collecting in the domain of stroke management.
- Aalst, W.: Process Mining. Data Science in Action. Springer, ??? (2016) Maggi et al. [2014] Maggi, F.M., DiFrancescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) Advanced Information Systems Engineering - 26th International Conference, CAiSE 2014, Thessaloniki, Greece, June 16-20, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8484, pp. 457–472. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-07881-6_31 . https://doi.org/10.1007/978-3-319-07881-6_31 Teinemaa et al. [2019] Teinemaa, I., Dumas, M., LaRosa, M., Maggi, F.M.: Outcome-oriented predictive process monitoring: Review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 17–11757 (2019) https://doi.org/10.1145/3301300 Xu et al. [2020] Xu, H., Pang, J., Yang, X., Li, M., Zhao, D.: Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients. BMC Medical Informatics Decis. Mak. 20-S(3), 120 (2020) https://doi.org/10.1186/s12911-020-1111-6 Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Maggi, F.M., DiFrancescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) Advanced Information Systems Engineering - 26th International Conference, CAiSE 2014, Thessaloniki, Greece, June 16-20, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8484, pp. 457–472. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-07881-6_31 . https://doi.org/10.1007/978-3-319-07881-6_31 Teinemaa et al. [2019] Teinemaa, I., Dumas, M., LaRosa, M., Maggi, F.M.: Outcome-oriented predictive process monitoring: Review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 17–11757 (2019) https://doi.org/10.1145/3301300 Xu et al. [2020] Xu, H., Pang, J., Yang, X., Li, M., Zhao, D.: Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients. BMC Medical Informatics Decis. Mak. 20-S(3), 120 (2020) https://doi.org/10.1186/s12911-020-1111-6 Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Teinemaa, I., Dumas, M., LaRosa, M., Maggi, F.M.: Outcome-oriented predictive process monitoring: Review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 17–11757 (2019) https://doi.org/10.1145/3301300 Xu et al. [2020] Xu, H., Pang, J., Yang, X., Li, M., Zhao, D.: Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients. BMC Medical Informatics Decis. Mak. 20-S(3), 120 (2020) https://doi.org/10.1186/s12911-020-1111-6 Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xu, H., Pang, J., Yang, X., Li, M., Zhao, D.: Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients. BMC Medical Informatics Decis. Mak. 20-S(3), 120 (2020) https://doi.org/10.1186/s12911-020-1111-6 Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Maggi, F.M., DiFrancescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) Advanced Information Systems Engineering - 26th International Conference, CAiSE 2014, Thessaloniki, Greece, June 16-20, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8484, pp. 457–472. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-07881-6_31 . https://doi.org/10.1007/978-3-319-07881-6_31 Teinemaa et al. [2019] Teinemaa, I., Dumas, M., LaRosa, M., Maggi, F.M.: Outcome-oriented predictive process monitoring: Review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 17–11757 (2019) https://doi.org/10.1145/3301300 Xu et al. [2020] Xu, H., Pang, J., Yang, X., Li, M., Zhao, D.: Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients. BMC Medical Informatics Decis. Mak. 20-S(3), 120 (2020) https://doi.org/10.1186/s12911-020-1111-6 Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Teinemaa, I., Dumas, M., LaRosa, M., Maggi, F.M.: Outcome-oriented predictive process monitoring: Review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 17–11757 (2019) https://doi.org/10.1145/3301300 Xu et al. [2020] Xu, H., Pang, J., Yang, X., Li, M., Zhao, D.: Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients. BMC Medical Informatics Decis. Mak. 20-S(3), 120 (2020) https://doi.org/10.1186/s12911-020-1111-6 Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xu, H., Pang, J., Yang, X., Li, M., Zhao, D.: Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients. BMC Medical Informatics Decis. Mak. 20-S(3), 120 (2020) https://doi.org/10.1186/s12911-020-1111-6 Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Teinemaa, I., Dumas, M., LaRosa, M., Maggi, F.M.: Outcome-oriented predictive process monitoring: Review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 17–11757 (2019) https://doi.org/10.1145/3301300 Xu et al. [2020] Xu, H., Pang, J., Yang, X., Li, M., Zhao, D.: Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients. BMC Medical Informatics Decis. Mak. 20-S(3), 120 (2020) https://doi.org/10.1186/s12911-020-1111-6 Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xu, H., Pang, J., Yang, X., Li, M., Zhao, D.: Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients. BMC Medical Informatics Decis. Mak. 20-S(3), 120 (2020) https://doi.org/10.1186/s12911-020-1111-6 Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Xu, H., Pang, J., Yang, X., Li, M., Zhao, D.: Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients. BMC Medical Informatics Decis. Mak. 20-S(3), 120 (2020) https://doi.org/10.1186/s12911-020-1111-6 Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need (2017) Bukhsh et al. [2021] Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: Predictive Business Process Monitoring with Transformer Network (2021) Bottrighi et al. [2016] Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Bottrighi, A., Canensi, L., Leonardi, G., Montani, S., Terenziani, P.: Trace retrieval for business process operational support. Expert Syst. Appl. 55, 212–221 (2016) Le et al. [2012] Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, Incorporating Applications and Innovations in Intelligent Systems XX: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11-13, 2012, pp. 179–192. Springer, ??? (2012) Lakshmanan et al. [2015] Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126 (2015) Cabanillas et al. [2014] Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Cabanillas, C., DiCiccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S.W., Soffer, P., Völzer, H. (eds.) Business Process Management - 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8659, pp. 424–432. Springer, ??? (2014). https://doi.org/10.1007/978-3-319-10172-9_31 . https://doi.org/10.1007/978-3-319-10172-9_31 Mehdiyev et al. [2017] Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: Loucopoulos, P., Manolopoulos, Y., Pastor, O., Theodoulidis, B., Zdravkovic, J. (eds.) 19th IEEE Conference on Business Informatics, CBI 2017, Thessaloniki, Greece, July 24-27, 2017, Volume 1: Conference Papers, pp. 119–128. IEEE Computer Society, ??? (2017) Hinton and Salakhutdinov [2006] Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality ofdata with neural networks. Science 313, 504–507 (2006) Alom et al. [2019] Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., Asari, V.K.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3) (2019) Mauro et al. [2019] Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Mauro, N.D., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019 - Advances in Artificial Intelligence - XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19-22, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11946, pp. 348–361. Springer, ??? (2019) Pascanu et al. [2014] Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997) Evermann et al. [2017] Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems 100, 129–140 (2017) Tax et al. [2018] Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Tax, N., Teinemaa, I., Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. CoRR abs/1811.00062 (2018) Camargo et al. [2019] Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate LSTM models of business processes. In: Hildebrandt, T.T., Dongen, B.F., Röglinger, M., Mendling, J. (eds.) Business Process Management - 17th International Conference, BPM 2019, Vienna, Austria, September 1-6, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11675, pp. 286–302. Springer, ??? (2019) Tax et al. [2017] Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering (CAiSE), pp. 477–492. Springer, ??? (2017) Cho et al. [2014] Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Hinkka et al. [2018] Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Hinkka, M., Lehto, T., Heljanko, K., Jung, A.: Classifying process instances using recurrent neural networks. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) Business Process Management Workshops - BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers. Lecture Notes in Business Information Processing, vol. 342, pp. 313–324. Springer, ??? (2018) Khan et al. [2018] Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Khan, M.A., Le, H., Do, K., Tran, T., Ghose, A., Dam, K.H., Sindhgatta, R.: Memory-augmented neural networks for predictive process analytics. CoRR abs/1802.00938 (2018) 1802.00938 Philipp et al. [2020] Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, February 19-21, 2020, pp. 225–230. IEEE, ??? (2020) Mialon et al. [2021] Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Mialon, G., Chen, D., Selosse, M., Mairal, J.: Graphit: Encoding graph structure in transformers. arXiv preprint arXiv:2106.05667 (2021) Dwivedi et al. [2020] Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 (2020) Di Francescomarino et al. [2017] Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Business Process Management: 15th International Conference, BPM 2017, Barcelona, Spain, September 10–15, 2017, Proceedings 15, pp. 252–268 (2017). Springer Liu et al. [2020] Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., Wang, P.: K-BERT: Enabling Language Representation with Knowledge Graph, pp. 2901–2908 (2020). Cited by: 308 Shang et al. [2019] Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation. CoRR abs/1906.00346 (2019) 1906.00346 Ba et al. [2016] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016) Xiong et al. [2020] Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., Liu, T.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524–10533 (2020). PMLR Lanza et al. [2017] Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Lanza, G., Setacci, C., Ricci, S., Castelli, P., Cremonesi, A., Lanza, J., Novali, C., Pratesi, C., Santalucia, P., Speziale, F., Zaninelli, A., Gensini, G.F.: An update of the italian stroke organization-stroke prevention awareness diffusion group guidelines on carotid endarterectomy and stenting: A personalized medicine approach. International journal of stroke : official journal of the International Stroke Society 12(5), 560–567 (2017) https://doi.org/10.1177/1747493017694395 Belkin and Niyogi [2003] Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003) Dwivedi and Bresson [2021] Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. AAAI Workshop on Deep Learning on Graphs: Methods and Applications (2021) Akiba et al. [2019] Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) Italian-Stroke-Association [2023] Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Italian-Stroke-Association: Current Guidelines. https://isa-aii.com/linee-guida/linee-guida-attuali/ Accessed 2023-09-04 Keahey et al. [2020] Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H.S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., Stubbs, J.: Lessons learned from the chameleon testbed. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association, ??? (2020) van Dongen [2012] Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1 Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1
- Dongen, B.: BPI Challenge 2012. Eindhoven University of Technology (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F . https://data.4tu.nl/articles/_/12689204/1