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A Foundational Framework and Methodology for Personalized Early and Timely Diagnosis (2311.16195v1)
Published 26 Nov 2023 in cs.LG and cs.AI
Abstract: Early diagnosis of diseases holds the potential for deep transformation in healthcare by enabling better treatment options, improving long-term survival and quality of life, and reducing overall cost. With the advent of medical big data, advances in diagnostic tests as well as in machine learning and statistics, early or timely diagnosis seems within reach. Early diagnosis research often neglects the potential for optimizing individual diagnostic paths. To enable personalized early diagnosis, a foundational framework is needed that delineates the diagnosis process and systematically identifies the time-dependent value of various diagnostic tests for an individual patient given their unique characteristics. Here, we propose the first foundational framework for early and timely diagnosis. It builds on decision-theoretic approaches to outline the diagnosis process and integrates machine learning and statistical methodology for estimating the optimal personalized diagnostic path. To describe the proposed framework as well as possibly other frameworks, we provide essential definitions. The development of a foundational framework is necessary for several reasons: 1) formalism provides clarity for the development of decision support tools; 2) observed information can be complemented with estimates of the future patient trajectory; 3) the net benefit of counterfactual diagnostic paths and associated uncertainties can be modeled for individuals 4) 'early' and 'timely' diagnosis can be clearly defined; 5) a mechanism emerges for assessing the value of technologies in terms of their impact on personalized early diagnosis, resulting health outcomes and incurred costs. Finally, we hope that this foundational framework will unlock the long-awaited potential of timely diagnosis and intervention, leading to improved outcomes for patients and higher cost-effectiveness for healthcare systems.
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[34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Laudicella, M., Walsh, B., Burns, E. & Smith, P. C. Cost of care for cancer patients in england: evidence from population-based patient-level data. Br. J. Cancer 114, 1286–1292 (2016). [3] Cobo-Calvo, A. et al. Association of very early treatment initiation with the risk of long-term disability in patients with a first demyelinating event. Neurology 101, e1280–e1292 (2023). [4] Aletaha, D. & Smolen, J. S. Diagnosis and management of rheumatoid arthritis: A review. JAMA 320, 1360–1372 (2018). [5] New 10-year survival by stage estimates for east of england. https://www.cancerresearchuk.org/health-professional/health-professional-news/new-10-year-survival-by-stage-estimates-for-east-of-england (2021). Accessed: 2023-11-20. [6] Sennfält, S. et al. The path to diagnosis in ALS: delay, referrals, alternate diagnoses, and clinical progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 24, 45–53 (2023). [7] Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cobo-Calvo, A. et al. Association of very early treatment initiation with the risk of long-term disability in patients with a first demyelinating event. Neurology 101, e1280–e1292 (2023). [4] Aletaha, D. & Smolen, J. S. Diagnosis and management of rheumatoid arthritis: A review. JAMA 320, 1360–1372 (2018). [5] New 10-year survival by stage estimates for east of england. https://www.cancerresearchuk.org/health-professional/health-professional-news/new-10-year-survival-by-stage-estimates-for-east-of-england (2021). Accessed: 2023-11-20. [6] Sennfält, S. et al. The path to diagnosis in ALS: delay, referrals, alternate diagnoses, and clinical progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 24, 45–53 (2023). [7] Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aletaha, D. & Smolen, J. S. Diagnosis and management of rheumatoid arthritis: A review. JAMA 320, 1360–1372 (2018). [5] New 10-year survival by stage estimates for east of england. https://www.cancerresearchuk.org/health-professional/health-professional-news/new-10-year-survival-by-stage-estimates-for-east-of-england (2021). Accessed: 2023-11-20. [6] Sennfält, S. et al. The path to diagnosis in ALS: delay, referrals, alternate diagnoses, and clinical progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 24, 45–53 (2023). [7] Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. 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When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. 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Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. 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[27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. 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Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) 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Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). 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Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. 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A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. 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[23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. 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S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. 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[24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. 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[34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. 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[8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cobo-Calvo, A. et al. Association of very early treatment initiation with the risk of long-term disability in patients with a first demyelinating event. Neurology 101, e1280–e1292 (2023). [4] Aletaha, D. & Smolen, J. S. Diagnosis and management of rheumatoid arthritis: A review. JAMA 320, 1360–1372 (2018). [5] New 10-year survival by stage estimates for east of england. https://www.cancerresearchuk.org/health-professional/health-professional-news/new-10-year-survival-by-stage-estimates-for-east-of-england (2021). Accessed: 2023-11-20. [6] Sennfält, S. et al. The path to diagnosis in ALS: delay, referrals, alternate diagnoses, and clinical progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 24, 45–53 (2023). [7] Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aletaha, D. & Smolen, J. S. Diagnosis and management of rheumatoid arthritis: A review. JAMA 320, 1360–1372 (2018). [5] New 10-year survival by stage estimates for east of england. https://www.cancerresearchuk.org/health-professional/health-professional-news/new-10-year-survival-by-stage-estimates-for-east-of-england (2021). Accessed: 2023-11-20. [6] Sennfält, S. et al. The path to diagnosis in ALS: delay, referrals, alternate diagnoses, and clinical progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 24, 45–53 (2023). [7] Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). New 10-year survival by stage estimates for east of england. https://www.cancerresearchuk.org/health-professional/health-professional-news/new-10-year-survival-by-stage-estimates-for-east-of-england (2021). Accessed: 2023-11-20. [6] Sennfält, S. et al. The path to diagnosis in ALS: delay, referrals, alternate diagnoses, and clinical progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 24, 45–53 (2023). [7] Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. 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[23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. 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[37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dicpinigaitis, P. V. 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Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. 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A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). New 10-year survival by stage estimates for east of england. https://www.cancerresearchuk.org/health-professional/health-professional-news/new-10-year-survival-by-stage-estimates-for-east-of-england (2021). Accessed: 2023-11-20. [6] Sennfält, S. et al. The path to diagnosis in ALS: delay, referrals, alternate diagnoses, and clinical progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 24, 45–53 (2023). [7] Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Sennfält, S. et al. The path to diagnosis in ALS: delay, referrals, alternate diagnoses, and clinical progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 24, 45–53 (2023). [7] Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) 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[12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. 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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). 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S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. 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Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). 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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. 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Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. 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Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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[23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. 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S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). 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J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) 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Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) 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Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. 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Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020).
- Diagnosis and management of rheumatoid arthritis: A review. JAMA 320, 1360–1372 (2018). [5] New 10-year survival by stage estimates for east of england. https://www.cancerresearchuk.org/health-professional/health-professional-news/new-10-year-survival-by-stage-estimates-for-east-of-england (2021). Accessed: 2023-11-20. [6] Sennfält, S. et al. The path to diagnosis in ALS: delay, referrals, alternate diagnoses, and clinical progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 24, 45–53 (2023). [7] Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. 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Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). New 10-year survival by stage estimates for east of england. https://www.cancerresearchuk.org/health-professional/health-professional-news/new-10-year-survival-by-stage-estimates-for-east-of-england (2021). Accessed: 2023-11-20. [6] Sennfält, S. et al. The path to diagnosis in ALS: delay, referrals, alternate diagnoses, and clinical progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 24, 45–53 (2023). [7] Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Sennfält, S. et al. The path to diagnosis in ALS: delay, referrals, alternate diagnoses, and clinical progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 24, 45–53 (2023). [7] Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). 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[12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). 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Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. 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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. 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Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. 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Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. 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[23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. 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Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). 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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) 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[12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). 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Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. 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[34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. 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Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. 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[23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. 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Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. 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S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020).
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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hanna, T. P. et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020). [8] de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. 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[21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). de Couto, G., Ouzounian, M. & Liu, P. P. Early detection of myocardial dysfunction and heart failure. Nat. Rev. Cardiol. 7, 334–344 (2010). [9] Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Crosby, D. et al. A roadmap for the early detection and diagnosis of cancer. Lancet Oncol. 21, 1397–1399 (2020). [10] Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015). [11] Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020). [12] Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). 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Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. 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Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). US Preventive Services Task Force et al. 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[21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. 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[20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. 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Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. 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(eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. 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Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). 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Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. 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Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. 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[17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. 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[20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. 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[33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. 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(eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. 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Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). 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(eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. 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M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nordberg, A. Dementia in 2014. towards early diagnosis in alzheimer disease. Nat. Rev. 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Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. 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Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. 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[19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. 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(eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). 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Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. 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(eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). 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Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). 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Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. 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[19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. 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A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. 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Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. 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Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. 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Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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(eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. 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Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022). [13] Doubeni, C. A. & Castle, P. E. Multicancer early detection: A promise yet to be proven. Am. Fam. Physician 107, 224–225A (2023). [14] US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. 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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). 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Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). US Preventive Services Task Force et al. Screening for thyroid cancer: US preventive services task force recommendation statement. JAMA 317, 1882–1887 (2017). [15] Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. BMJ Open 4, e004439 (2014). [16] Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. 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[33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. 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(eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. 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Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). 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Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. 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(eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. 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[20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. 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Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) 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Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. 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Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) 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Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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[38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. 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A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. 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Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). 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Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dhedhi, S. A., Swinglehurst, D. & Russell, J. ’timely’ diagnosis of dementia: what does it mean? a narrative analysis of GPs’ accounts. 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[23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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[38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. 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A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. 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Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). 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When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aristotle. Nicomachean Ethics (Translated by W. D. Ross with an Introduction by R. W. Browne) (DIGIREADS.COM, 2016). [17] Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. 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Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. 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Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. 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Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. 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Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. 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Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). 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J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) 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Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) 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Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. 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Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020).
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[23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. 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Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Cohen, B. J. Is expected utility theory normative for medical decision making? Med. Decis. Making 16, 1–6 (1996). [18] Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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[34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). 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Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. 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Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). 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Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020).
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Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Klir, G. J. Uncertainty and Information: Foundations of Generalized Information Theory Vol. 35 (Emerald Group Publishing Limited, 2006). [19] Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Rao, G., Epner, P., Bauer, V., Solomonides, A. & Newman-Toker, D. E. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 4, 67–72 (2017). [20] World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). 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Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. 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Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. 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[27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. 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Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. 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Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. 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[37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dicpinigaitis, P. V. 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Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020).
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[27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. 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Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). World Health Organization. Guide to Cancer Early Diagnosis (World Health Organization, 2017). [21] Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). 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Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. 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[42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020).
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Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dobson, R. & Giovannoni, G. Multiple sclerosis - a review. Eur. J. Neurol. 26, 27–40 (2019). [22] Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. 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Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. 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Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. 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Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. 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Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. 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Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. 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Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 8, 371–386 (2021). [23] Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). 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M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. 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[46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Wijsenbeek, M., Suzuki, A. & Maher, T. M. Interstitial lung diseases. Lancet 400, 769–786 (2022). [24] Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. 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Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Jain, B. The key role of differential diagnosis in diagnosis. Diagnosis (Berl) 4, 239–240 (2017). [25] Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Aberegg, S. K. & Johnson, S. A. When alternative diagnoses are more likely than pulmonary embolism: A paradox. Ann. Am. Thorac. Soc. 17, 670–672 (2020). [26] Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. 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[37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). 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Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. 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Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). 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B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. 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Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. 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A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. 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Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. 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[46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? 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Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. 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Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. 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(eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020).
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Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Winkler, R. L. Ambiguity, probability, preference, and decision analysis. J. Risk Uncertain. 4, 285–297 (1991). [27] Hatch, S. Snowball in a blizzard: A physician’s notes on uncertainty in medicine (New York: Basic Books, a member of the Perseus Books Group, New York, NY, US, 2016). [28] Kennedy, A. G. Managing uncertainty in diagnostic practice. J. Eval. Clin. Pract. 23, 959–963 (2017). [29] van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. BMC Fam. Pract. 22, 151 (2021). [33] Dicpinigaitis, P. V. Angiotensin-converting enzyme inhibitor-induced cough: ACCP evidence-based clinical practice guidelines. Chest 129, 169S–173S (2006). [34] Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. Clinically-Inspired Multi-Agent transformers for disease trajectory forecasting from multimodal data. IEEE Trans. Med. Imaging PP (2023). [41] Alaa, A. M. & van der Schaar, M. Attentive state-space modeling of disease progression, NeurIPS (Advances in Neural Information Processing Systems, 2019). [42] Williamson, T. & Ravani, P. Marginal structural models in clinical research: when and how to use them? Nephrol. Dial. Transplant 32, ii84–ii90 (2017). [43] Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) 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Estimating counterfactual treatment outcomes over time through adversarially balanced representations, ICLR (International Conference on Learning Representations, 2020). [44] Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Chaudhuri, K. et al. (eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Elstein, A. S. & Schwartz, A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 324, 729–732 (2002). [35] Hull, M. A., Rees, C. J., Sharp, L. & Koo, S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat. Rev. Gastroenterol. Hepatol. 17, 773–780 (2020). [36] Sukkar, R., Katz, E., Zhang, Y., Raunig, D. & Wyman, B. T. Disease progression modeling using hidden markov models, IEEE, 2845–2848 (IEEE, 2012). [37] Putter, H. & van Houwelingen, H. C. Landmarking 2.0: Bridging the gap between joint models and landmarking. Stat. Med. 41, 1901–1917 (2022). [38] Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). van der Schaar, M. Medicine 2.0: Transforming clinical practice and discovery through machine learning and electronic health engines. Oon Lecture (2018). [30] Nuffield Council on Bioethics. Dementia: ethical issues. https://www.nuffieldbioethics.org/assets/pdfs/Dementia-report-for-web.pdf (2009). Accessed: 2023-11-20. [31] Ginsberg, M. L. Counterfactuals. Artif. Intell. 30, 35–79 (1986). [32] Bergmann, M. et al. Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis. 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(eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Alaa, A. M. & van der Schaar, M. A hidden absorbing Semi-Markov model for informatively censored temporal data: Learning and inference. J. Mach. Learn. Res. 19, 1–62 (2018). [39] Wang, T., Qiu, R. G. & Yu, M. Predictive modeling of the progression of alzheimer’s disease with recurrent neural networks. Sci. Rep. 8, 9161 (2018). [40] Nguyen, H. H., Blaschko, M. B., Saarakkala, S. & Tiulpin, A. 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(eds) Continuous-Time modeling of counterfactual outcomes using neural controlled differential equations. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 19497–19521 (PMLR, 2022). [45] Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Tillotson, G. PK-PD compass, a novel computerized decision support system. Lancet Infect. Dis. 17, 908 (2017). [46] Frymoyer, A. et al. Model-Informed precision dosing of vancomycin in hospitalized children: Implementation and adoption at an academic children’s hospital. Front. Pharmacol. 11, 551 (2020). Frymoyer, A. et al. 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