Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment.

Autor:	Otten M; Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.; Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands., Jagesar AR; Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.; Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands., Dam TA; Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.; Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands., Biesheuvel LA; Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.; Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands., den Hengst F; Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands., Ziesemer KA; University Library, Vrije Universiteit, Amsterdam, The Netherlands., Thoral PJ; Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., de Grooth HJ; Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., Girbes ARJ; Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., François-Lavet V; Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands., Hoogendoorn M; Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands., Elbers PWG; Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
Jazyk:	angličtina
Zdroj:	Critical care medicine [Crit Care Med] 2024 Feb 01; Vol. 52 (2), pp. e79-e88. Date of Electronic Publication: 2023 Nov 08.
DOI:	10.1097/CCM.0000000000006100
Abstrakt:	Objective: Reinforcement learning (RL) is a machine learning technique uniquely effective at sequential decision-making, which makes it potentially relevant to ICU treatment challenges. We set out to systematically review, assess level-of-readiness and meta-analyze the effect of RL on outcomes for critically ill patients. Data Sources: A systematic search was performed in PubMed, Embase.com, Clarivate Analytics/Web of Science Core Collection, Elsevier/SCOPUS and the Institute of Electrical and Electronics Engineers Xplore Digital Library from inception to March 25, 2022, with subsequent citation tracking. Data Extraction: Journal articles that used an RL technique in an ICU population and reported on patient health-related outcomes were included for full analysis. Conference papers were included for level-of-readiness assessment only. Descriptive statistics, characteristics of the models, outcome compared with clinician's policy and level-of-readiness were collected. RL-health risk of bias and applicability assessment was performed. Data Synthesis: A total of 1,033 articles were screened, of which 18 journal articles and 18 conference papers, were included. Thirty of those were prototyping or modeling articles and six were validation articles. All articles reported RL algorithms to outperform clinical decision-making by ICU professionals, but only in retrospective data. The modeling techniques for the state-space, action-space, reward function, RL model training, and evaluation varied widely. The risk of bias was high in all articles, mainly due to the evaluation procedure. Conclusion: In this first systematic review on the application of RL in intensive care medicine we found no studies that demonstrated improved patient outcomes from RL-based technologies. All studies reported that RL-agent policies outperformed clinician policies, but such assessments were all based on retrospective off-policy evaluation. Competing Interests: Dr. Dam’s institution received funding from ZonMW/Netherlands Organization for Health Research and Development (10430012010003); he received funding from Pacmed BV. Dr. Hengst received funding from ING Bank N.V. Dr. Hoogendoorn disclosed co-ownership of PersonalAIze B.V. The remaining authors have disclosed that they do not have any potential conflicts of interest. (Copyright © 2023 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)
Databáze:	MEDLINE
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