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Knowledge Enhanced Conditional Imputation for Healthcare Time-series
Published 27 Dec 2023 in cs.LG and cs.AI | (2312.16713v4)
Abstract: We introduce the Conditional Self-Attention Imputation (CSAI), a novel recurrent neural network architecture designed to address the challenges of complex missing data patterns in multivariate time series derived from hospital electronic health records (EHRs). CSAI extends the current state-of-the-art neural network-based imputation methods by introducing key modifications specifically adapted to EHR data characteristics, namely: a) an attention-based hidden state initialisation technique to capture both long- and short-range temporal dependencies prevalent in EHRs, b) a domain-informed temporal decay mechanism to adjust the imputation process to clinical data recording patterns, and c) a non-uniform masking strategy that models non-random missingness by calibrating weights according to both temporal and cross-sectional data characteristics. Comprehensive evaluation across four EHR benchmark datasets demonstrate CSAI's effectiveness compared to state-of-the-art neural architectures in data restoration and downstream predictive tasks. Additionally, CSAI is integrated within PyPOTS, an open-source Python toolbox designed for machine learning tasks on partially observed time series. This work significantly advances the state of neural network imputation applied to EHRs by more closely aligning algorithmic imputation with clinical realities.
- Ibrahim, Z.M., Bean, D., Searle, T., Qian, L., Wu, H., Shek, A., Kraljevic, Z., Galloway, J., Norton, S., Teo, J.T.H., Dobson, R.J.: A knowledge distillation ensemble framework for predicting short- and long-term hospitalization outcomes from electronic health records data. IEEE Journal of Biomedical and Health Informatics 26(1), 423–435 (2022) https://doi.org/10.1109/JBHI.2021.3089287 Shamout et al. [2020] Shamout, F.E., Zhu, T., Sharma, P., Watkinson, P.J., Clifton, D.A.: Deep interpretable early warning system for the detection of clinical deterioration. IEEE Journal of Biomedical and Health Informatics 24(2), 437–446 (2020) https://doi.org/10.1109/JBHI.2019.2937803 Wells et al. [2013] Wells, B.J., Chagin, K.M., Nowacki, A.S., Kattan, M.W.: Strategies for handling missing data in electronic health record derived data. Egems 1(3) (2013) Hu and et al. [2017] Hu, Z., al.: Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record. Journal of biomedical informatics 68, 112–120 (2017) Mazurowski and et al. [2008] Mazurowski, M., al.: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural networks 21(2-3), 427–436 (2008) Wu et al. [2018] Wu, J., He, J., Liu, Y.: Imverde: Vertex-diminished random walk for learning imbalanced network representation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 871–880 (2018). IEEE et al. [2011] al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Shamout, F.E., Zhu, T., Sharma, P., Watkinson, P.J., Clifton, D.A.: Deep interpretable early warning system for the detection of clinical deterioration. IEEE Journal of Biomedical and Health Informatics 24(2), 437–446 (2020) https://doi.org/10.1109/JBHI.2019.2937803 Wells et al. [2013] Wells, B.J., Chagin, K.M., Nowacki, A.S., Kattan, M.W.: Strategies for handling missing data in electronic health record derived data. Egems 1(3) (2013) Hu and et al. [2017] Hu, Z., al.: Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record. Journal of biomedical informatics 68, 112–120 (2017) Mazurowski and et al. [2008] Mazurowski, M., al.: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural networks 21(2-3), 427–436 (2008) Wu et al. [2018] Wu, J., He, J., Liu, Y.: Imverde: Vertex-diminished random walk for learning imbalanced network representation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 871–880 (2018). IEEE et al. [2011] al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Wells, B.J., Chagin, K.M., Nowacki, A.S., Kattan, M.W.: Strategies for handling missing data in electronic health record derived data. Egems 1(3) (2013) Hu and et al. [2017] Hu, Z., al.: Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record. Journal of biomedical informatics 68, 112–120 (2017) Mazurowski and et al. [2008] Mazurowski, M., al.: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural networks 21(2-3), 427–436 (2008) Wu et al. [2018] Wu, J., He, J., Liu, Y.: Imverde: Vertex-diminished random walk for learning imbalanced network representation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 871–880 (2018). IEEE et al. [2011] al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Hu, Z., al.: Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record. Journal of biomedical informatics 68, 112–120 (2017) Mazurowski and et al. [2008] Mazurowski, M., al.: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural networks 21(2-3), 427–436 (2008) Wu et al. [2018] Wu, J., He, J., Liu, Y.: Imverde: Vertex-diminished random walk for learning imbalanced network representation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 871–880 (2018). IEEE et al. [2011] al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Wu, J., He, J., Liu, Y.: Imverde: Vertex-diminished random walk for learning imbalanced network representation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 871–880 (2018). IEEE et al. [2011] al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. 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In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Computational Cardiology 39, 245–248 (2012) Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. 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Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Hu, Z., al.: Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record. Journal of biomedical informatics 68, 112–120 (2017) Mazurowski and et al. [2008] Mazurowski, M., al.: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural networks 21(2-3), 427–436 (2008) Wu et al. [2018] Wu, J., He, J., Liu, Y.: Imverde: Vertex-diminished random walk for learning imbalanced network representation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 871–880 (2018). IEEE et al. [2011] al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mazurowski, M., al.: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural networks 21(2-3), 427–436 (2008) Wu et al. [2018] Wu, J., He, J., Liu, Y.: Imverde: Vertex-diminished random walk for learning imbalanced network representation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 871–880 (2018). IEEE et al. [2011] al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Wu, J., He, J., Liu, Y.: Imverde: Vertex-diminished random walk for learning imbalanced network representation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 871–880 (2018). IEEE et al. [2011] al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. 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[2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. 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Computational Cardiology 39, 245–248 (2012) al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. 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[2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. 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Computational Cardiology 39, 245–248 (2012) Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. 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Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. 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(eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. 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[2008] Mazurowski, M., al.: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural networks 21(2-3), 427–436 (2008) Wu et al. [2018] Wu, J., He, J., Liu, Y.: Imverde: Vertex-diminished random walk for learning imbalanced network representation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 871–880 (2018). IEEE et al. [2011] al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mazurowski, M., al.: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural networks 21(2-3), 427–436 (2008) Wu et al. [2018] Wu, J., He, J., Liu, Y.: Imverde: Vertex-diminished random walk for learning imbalanced network representation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 871–880 (2018). IEEE et al. [2011] al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Wu, J., He, J., Liu, Y.: Imverde: Vertex-diminished random walk for learning imbalanced network representation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 871–880 (2018). IEEE et al. [2011] al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. 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CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. 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Computational Cardiology 39, 245–248 (2012) al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. 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[2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Computational Cardiology 39, 245–248 (2012) Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. 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Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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(eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. 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[2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. 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[2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. 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In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. 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[2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. 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[2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. 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[2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. 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[2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) al., R.H.: Relationship between blood pressure and incident chronic kidney disease in hypertensive patients. Clinical Journal of the American Society of Nephrologists 6(11) (2011) Yadav et al. [2018] Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. 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[2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. 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[2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. 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Computational Cardiology 39, 245–248 (2012) al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. 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[2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. 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(eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. 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Computational Cardiology 39, 245–248 (2012) Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. 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Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. 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Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Computational Cardiology 39, 245–248 (2012) Yadav, P., Steinbach, M., Kumar, V., Simon, G.: Mining electronic health records (ehrs) a survey. ACM Computing Surveys (CSUR) 50(6), 1–40 (2018) Jensen et al. [2012] Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Computational Cardiology 39, 245–248 (2012) al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. 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[2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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[2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Computational Cardiology 39, 245–248 (2012) Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. 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Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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(eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Computational Cardiology 39, 245–248 (2012) Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Computational Cardiology 39, 245–248 (2012) Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. 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[2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Computational Cardiology 39, 245–248 (2012) Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. 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Computational Cardiology 39, 245–248 (2012) Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. 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[2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. 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[2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. 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[2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13(6), 395–405 (2012) et al. [2021] al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) al., T.E.: A survey on missing data in machine learning. Journal of Big Data 8, 140 (2021) Gu et al. [2018] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern recognition 77, 354–377 (2018) Schuster and Paliwal [1997] Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. 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[2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Computational Cardiology 39, 245–248 (2012) Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. 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Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. 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Computational Cardiology 39, 245–248 (2012) Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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[1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. 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In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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[2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. 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[2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45(11), 2673–2681 (1997) Arber et al. [1997] Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Arber, S., Hunter, J.J., Ross Jr, J., Hongo, M., Sansig, G., Borg, J., Perriard, J.-C., Chien, K.R., Caroni, P.: Mlp-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell 88(3), 393–403 (1997) Sauer et al. [2022] Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Computational Cardiology 39, 245–248 (2012) Sauer, C.M., Chen, L.-C., Hyland, S.L., Girbes, A., Elbers, P., Celi, L.A.: Leveraging electronic health records for data science: common pitfalls and how to avoid them. The Lancet Digital Health 4(12), 893–898 (2022) Cao et al. [2018] Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. 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IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. 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Computational Cardiology 39, 245–248 (2012) Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. 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[2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. 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[2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. 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Computational Cardiology 39, 245–248 (2012) Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. 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In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Computational Cardiology 39, 245–248 (2012) Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. 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[2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. 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Computational Cardiology 39, 245–248 (2012) Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31 (2018) Che et al. [2018] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. 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In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. 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(eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Computational Cardiology 39, 245–248 (2012) Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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[2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8(1), 1–12 (2018) Yoon et al. [2017] Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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[2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. 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Computational Cardiology 39, 245–248 (2012) Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. 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[2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. 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[2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012)
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In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Yoon, J., Zame, W.R., Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time Series Workshop in International Conference on Machine Learning (2017) Jun et al. [2020] Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Jun, E., Mulyadi, A.W., Choi, J., Suk, H.-I.: Uncertainty-gated stochastic sequential model for ehr mortality prediction. IEEE Transactions on Neural Networks and Learning Systems 32(9), 4052–4062 (2020) Mulyadi et al. [2021] Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mulyadi, A.W., Jun, E., Suk, H.-I.: Uncertainty-aware variational-recurrent imputation network for clinical time series. IEEE Transactions on Cybernetics (2021) Luo et al. [2018] Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems 31 (2018) Mescheder et al. [2018] Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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[2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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Computational Cardiology 39, 245–248 (2012) Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. 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Computational Cardiology 39, 245–248 (2012) Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. 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[2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. 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Computational Cardiology 39, 245–248 (2012) Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. 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[2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012)
- Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018). PMLR Tashiro et al. [2021] Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012)
- Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34, 24804–24816 (2021) Wen et al. [2022] Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv preprint arXiv:2202.07125 (2022) Du et al. [2023] Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219, 119619 (2023) Shan et al. [2023] Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Shan, S., Li, Y., Oliva, J.B.: Nrtsi: Non-recurrent time series imputation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). https://doi.org/10.1109/ICASSP49357.2023.10095054 Choi et al. [2023] Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Choi, T.-M., Kang, J.-S., Kim, J.-H.: Rdis: Random drop imputation with self-training for incomplete time series data. IEEE Access (2023) Qian et al. [2023] Qian, L., Ibrahim, Z.M., Zhang, A., Dobson, R.J.B.: Addressing class imbalance in electronic health records data imputation. In: Ibrahim, Z.M., Wu, H., Wiratunga, N. (eds.) Proceedings of the 6th International Workshop on Knowledge Discovery from Healthcare Data Co-located with 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, August 20, 2023. CEUR Workshop Proceedings, vol. 3479. CEUR-WS.org, ??? (2023). https://ceur-ws.org/Vol-3479/paper7.pdf Pollard et al. [2018] Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. 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- Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1–13 (2018) Johnson and et al. [2016] Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012)
- Johnson, A., al.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1–9 (2016) Purushotham et al. [2018] Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012)
- Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics 83, 112–134 (2018) Harutyunyan et al. [2019] Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012)
- Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Scientific data 6(1), 96 (2019) Silva et al. [2012] Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012) Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012)
- Silva, I., Moody, G., Scott, D., Celi, L., Mark, R.: Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. Computational Cardiology 39, 245–248 (2012)
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