[54] Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimic-iii, a freely accessible critical care database. Scientific data, 3(1):1-9, 2016.
[55] Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed, Leo Celi, Peter Szolovits, and Marzyeh Ghassemi. Deep reinforcement learning for sepsis treatment. arXiv preprint arXiv:1711.09602, 2017.
[56] Siqi Liu, Kay Choong See, Kee Yuan Ngiam, Leo Anthony Celi, Xingzhi Sun, and Mengling Feng. Reinforcement learning for clinical decision support in critical care: comprehensive review. Journal of medical Internet research, 22(7):e18477, 2020.
[57] Taylor W Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, and Marzyeh Ghassemi. An empirical study of representation learning for reinforcement learning in healthcare. arXiv preprint arXiv:2011.11235, 2020.
[58] Mervyn Singer, Clifford S Deutschman, Christopher Warren Seymour, Manu Shankar-Hari, Djillali Annane, Michael Bauer, Rinaldo Bellomo, Gordon R Bernard, Jean-Daniel Chiche, Craig M Coopersmith, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3). Jama, 315(8):801–810, 2016.
[59] Simon Lambden, Pierre Francois Laterre, Mitchell M Levy, and Bruno Francois. The sofa score—development, utility and challenges of accurate assessment in clinical trials. Critical Care, 23(1):1-9, 2019.
[60] Flavio Lopes Ferreira, Daliana Peres Bota, Annette Bross, Christian Mélot, and Jean-Louis Vincent. Serial evaluation of the sofa score to predict outcome in critically ill patients. Jama, 286(14):1754-1758, 2001.
[61] Jason Waechter, Anand Kumar, Stephen E Lapinsky, John Marshall, Peter Dodek, Yaseen Arabi, Joseph E Parrillo, R Phillip Dellinger, Allan Garland, Cooperative Antimicrobial Therapy of Septic Shock Database Research Group, et al. Interaction between fluids and vasoactive agents on mortality in septic shock: a multicenter, observational study. Critical care medicine, 42(10):2158-2168, 2014.
[62] Lihong Li and Michael L Littman. Lazy approximation for solving continuous finite-horizon mdps. In AAAI, volume 5, pages 1175-1180, 2005.
[63] Richard Dearden, Nicholas Meuleau, Richard Washington, and Zhengzhu Feng. Dynamic programming for structured continuous markov decision problems. In Twentieth Conference on Uncertainty in Artificial Intelligene (UAI-04), 2004.
[64] Prashan Madumal, Tim Miller, Liz Sonenberg, and Frank Vetere. Explainable reinforcement learning through a causal lens. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 2493-2500, 2020.
[65] Ioana Bica, Daniel Jarrett, Alihan Hüyük, and Mihaela van der Schaar. Learning" what-if" explanations for sequential decision-making. arXiv preprint arXiv:2007.13531, 2020.
[66] Amir-Hossein Karimi, Julius Von Kugelgen, Bernhard Scholkopf, and Isabel Valera. Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. Advances in neural information processing systems, 33:265-277, 2020.
[67] Stratis Tsirtsis and Manuel Gomez-Rodriguez. Decisions, counterfactual explanations and strategic behavior. Advances in Neural Information Processing Systems, 33:16749-16760, 2020.