cirpublications.com-6uaE-1790878115-d419235865 1790878115 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 2026 5 2 Yuki Yamamoto Kenji Ito 2026 10.68159/d419235865 https://cirpublications.com/pub/journal/1/article/d419235865 Tonna JE, Abrams D, Brodie D, Greenwood JC, Mateo-Sidron JA, Usman A, et al. Management of adult patients supported with venovenous extracorporeal membrane oxygenation (VV ECMO): guideline from the extracorporeal life support organization (ELSO). ASAIO J. 2021;67(6):601-10. 10.1097/MAT.0000000000001432 Lorusso R, Shekar K, MacLaren G, Schmidt M, Pellegrino V, Meyns B, et al. ELSO interim guidelines for venoarterial extracorporeal membrane oxygenation in adult cardiac patients. ASAIO J. 2021;67(8):827-44. 10.1097/MAT.0000000000001499 Song J, Dave SB, Yang Y, Foote H, Moore R, Upadhyaya P, et al. rECMOmender: reinforcement learning for decision support in venovenous extracorporeal membrane oxygenation management. Crit Care Explor. 2026;8(2):e1369. Pladet L, Luijken K, Fresiello L, Miranda DD, Hermens JA, Smeden MV, et al. Clinical decision support for extracorporeal membrane oxygenation: will we fly by wire? Perfusion. 2023;38(1 Suppl):68-81. 10.1177/02676591221113227 Cohen W, Mirzai S, Li Z, Combs P, Hu K, Rose R, et al. Personalized ECMO: crafting individualized support. J Cardiothorac Vasc Anesth. 2022;36(5):1477-86. 10.1053/j.jvca.2021.11.034 Ayers B, Wood K, Gosev I, Prasad S. Predicting survival after extracorporeal membrane oxygenation by using machine learning. Ann Thorac Surg. 2020;110(4):1193-200. 10.1016/j.athoracsur.2020.02.006 Peine A, Hallawa A, Bickenbach J, Dartmann G, Fazlic LB, Schmeink A, et al. Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care. NPJ Digit Med. 2021;4(1):32. 10.1038/s41746-021-00388-6 den Hengst F, Otten M, Elbers P, van Harmelen F, François-Lavet V, Hoogendoorn M. Guideline-informed reinforcement learning for mechanical ventilation in critical care. Artif Intell Med. 2024;147:102742. 10.1016/j.artmed.2023.102742 Liu S, Xu Q, Xu Z, Liu Z, Sun X, Xie G, et al. Reinforcement learning to optimize ventilator settings for patients on invasive mechanical ventilation: retrospective study. J Med Internet Res. 2024;26:e44494. 10.2196/44494 Roggeveen LF, Hassouni AE, de Grooth HJ, Girbes AR, Hoogendoorn M, Elbers PW, et al. Reinforcement learning for intensive care medicine: actionable clinical insights from novel approaches to reward shaping and off-policy model evaluation. Intensive Care Med Exp. 2024;12(1):32. 10.1186/s40635-024-00625-5 Kalimouttou A, Kennedy JN, Feng J, Singh H, Saria S, Angus DC, et al. Optimal vasopressin initiation in septic shock: the OVISS reinforcement learning study. JAMA. 2025;333(19):1688-98. 10.1001/jama.2025.4738 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 Bologheanu R, Kapral L, Laxar D, Maleczek M, Dibiasi C, Zeiner S, et al. Development of a reinforcement learning algorithm to optimize corticosteroid therapy in critically ill patients with sepsis. J Clin Med. 2023;12(4):1513. 10.3390/jcm12041513 Liu S, See KC, Ngiam KY, Celi LA, Sun X, Feng M. Reinforcement learning for clinical decision support in critical care: comprehensive review. J Med Internet Res. 2020;22(7):e18477. 10.2196/18477 Jayaraman P, Desman J, Sabounchi M, Nadkarni GN, Sakhuja A. A primer on reinforcement learning in medicine for clinicians. NPJ Digit Med. 2024;7(1):337. 10.1038/s41746-024-01269-4 Yu C, Liu J, Nemati S, Yin G. Reinforcement learning in healthcare: a survey. ACM Comput Surv. 2021;55(1):1-36. 10.1145/3477600 Lee HY, Chung S, Hyeon D, Yang HL, Lee HC, Ryu HG, et al. Reinforcement learning model for optimizing dexmedetomidine dosing to prevent delirium in critically ill patients. NPJ Digit Med. 2024;7(1):325. 10.1038/s41746-024-01340-0 Hsu JC, Pai CH, Lin LY, Wang CH, Wei LY, Chen JW, et al. Machine learning-based first-day mortality prediction for venoarterial extracorporeal membrane oxygenation: the novel RESCUE-24 score. ASAIO J. 2026;72(2):117-28. Kalra A, Bachina P, Shou BL, Hwang J, Barshay M, Kulkarni S, et al. Using machine learning to predict neurologic injury in venovenous extracorporeal membrane oxygenation recipients: an ELSO registry analysis. JTCVS Open. 2024;21:140-67. 10.1016/j.xjon.2024.07.015 Kalra A, Bachina P, Shou BL, Hwang J, Barshay M, Kulkarni S, et al. Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: an extracorporeal life support organization registry analysis. JTCVS Open. 2024;20:64-88. 10.1016/j.xjon.2024.05.009 Gao H, Huang X, Zhou K, Ling Y, Chen Y, Mou C, et al. Development and validation of a machine learning model for predicting mortality risk in veno-arterial extracorporeal membrane oxygenation patients. Sci Rep. 2025;15(1):41581. 10.1038/s41598-025-41581-0 Leng A, Bachina P, Liu O, Shou B, Racz C, Giliver DA, et al. Enhancing survival prediction after venoarterial extracorporeal membrane oxygenation using machine learning. ASAIO J. 2025;71(6):10-97. Chen S, Qiu X, Tan X, Fang Z, Jin Y. A model-based hybrid soft actor-critic deep reinforcement learning algorithm for optimal ventilator settings. Inf Sci. 2022;611:47-64. 10.1016/j.ins.2022.08.032 Lee H, Yoon HK, Kim J, Park JS, Koo CH, Won D, et al. Development and validation of a reinforcement learning model for ventilation control during emergence from general anesthesia. NPJ Digit Med. 2023;6(1):145. 10.1038/s41746-023-00891-1 Yu C, Liu J, Zhao H. Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units. BMC Med Inform Decis Mak. 2019;19(Suppl 2):57. 10.1186/s12911-019-0795-0 Yu C, Ren G, Dong Y. Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units. BMC Med Inform Decis Mak. 2020;20(Suppl 3):124. 10.1186/s12911-020-01145-y Drudi C, Mollura M, Li-wei HL, Barbieri R. A reinforcement learning model for optimal treatment strategies in intensive care: assessment of the role of cardiorespiratory features. IEEE Open J Eng Med Biol. 2024;5:806-15. 10.1109/OJEMB.2024.3366845 Wu X, Li R, He Z, Yu T, Cheng C. A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis. NPJ Digit Med. 2023;6(1):15. 10.1038/s41746-023-00773-6 Zhang T, Qu Y, Wang D, Zhong M, Cheng Y, Zhang M. Optimizing sepsis treatment strategies via a reinforcement learning model. Biomed Eng Lett. 2024;14(2):279-89. 10.1007/s13534-023-00327-6 Choi Y, Oh S, Huh JW, Joo HT, Lee H, You W, et al. Deep reinforcement learning extracts the optimal sepsis treatment policy from treatment records. Commun Med (Lond). 2024;4(1):245. 10.1038/s43856-024-00662-8