Reinforcement Learning for Intravenous Fluid Resuscitation in Septic Shock: A Position Paper on Safety Constraints, Reward Design, and Clinical Oversight

cirpublications.com-67O1-1790880684-p764100943 1790880684 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2022 1 1 Reinforcement Learning for Intravenous Fluid Resuscitation in Septic Shock: A Position Paper on Safety Constraints, Reward Design, and Clinical Oversight Claire Martin Julien Robert Sophie Bernard Antoine Girard 2022 10.68159/p764100943 https://cirpublications.com/pub/journal/1/article/p764100943 Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Crit Care Med. 2021;49(11):e1063-e1143. 10.1097/CCM.0000000000005337 Macdonald S. Fluid resuscitation in patients presenting with sepsis: current insights. Open Access Emerg Med. 2022;14:633-8. 10.2147/OAEM.S319777 Jia Y, Burden J, Lawton T, Habli I. Safe reinforcement learning for sepsis treatment. In: 2020 IEEE Int Conf Healthc Inform. 2020. p. 1-7. 10.1109/ICHI48887.2020.9374387 Raghu A, Komorowski M, Ahmed I, Celi LA, Szolovits P, Ghassemi M. Reinforcement learning for sepsis treatment: baselines and analysis. In: Mach Learn Healthc Conf Proc. 2017;68:241-52. Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716-20. 10.1038/s41591-018-0213-5 Mollura M, Drudi C, Lehman LW, Barbieri R. A reinforcement learning application for optimal fluid and vasopressor interventions in septic ICU patients. In: 2022 44th Annu Int Conf IEEE Eng Med Biol Soc. 2022. p. 321-324. 10.1109/EMBC48229.2022.9870975 Su L, Li Y, Liu S, Zhang S, Zhou X, Weng L, et al. Establishment and implementation of potential fluid therapy balance strategies for ICU sepsis patients based on reinforcement learning. Front Med (Lausanne). 2022;9:766447. 10.3389/fmed.2022.766447 Liu R, Greenstein JL, Fackler JC, Bergmann J, Bembea MM, Winslow RL. Offline reinforcement learning with uncertainty for treatment strategies in sepsis. arXiv. 2021;2107.04491. Nanayakkara T, Clermont G, Langmead CJ, Swigon D. Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. PLOS Digit Health. 2022;1(2):e0000012. 10.1371/journal.pdig.0000012 Huang Y, Cao R, Rahmani A. Reinforcement learning for sepsis treatment: a continuous action space solution. In: Mach Learn Healthc Conf Proc. 2022;193:631-47. Yu C, Ren G, Liu J. Deep inverse reinforcement learning for sepsis treatment. In: 2019 IEEE Int Conf Healthc Inform. 2019. p. 1-3. 10.1109/ICHI.2019.8904727 Kim HI, Park S. Sepsis: early recognition and optimized treatment. Tuberc Respir Dis (Seoul). 2019;82(1):6-14. 10.4046/trd.2017.0041 Oberst M, Sontag D. Counterfactual off-policy evaluation with gumbel-max structural causal models. In: Proc Int Conf Mach Learn. 2019;97:4881-90.