Please use this identifier to cite or link to this item: https://doi.org/10.2196/18477
Title: Reinforcement learning for clinical decision support in critical care: Comprehensive review
Authors: Liu, S. 
See, K.C. 
Ngiam, K.Y. 
Celi, L.A.
Sun, X.
Feng, M. 
Keywords: Artificial intelligence
Critical care
Decision support systems
clinical
Intensive care unit
Machine learning
Reinforcement learning
Issue Date: 20-Jul-2020
Publisher: JMIR Publications Inc.
Citation: Liu, S., See, K.C., Ngiam, K.Y., Celi, L.A., Sun, X., Feng, M. (2020-07-20). Reinforcement learning for clinical decision support in critical care: Comprehensive review. Journal of Medical Internet Research 22 (7) : e18477. ScholarBank@NUS Repository. https://doi.org/10.2196/18477
Rights: Attribution 4.0 International
Abstract: Background: Decision support systems based on reinforcement learning (RL) have been implemented to facilitate the delivery of personalized care. This paper aimed to provide a comprehensive review of RL applications in the critical care setting. Objective: This review aimed to survey the literature on RL applications for clinical decision support in critical care and to provide insight into the challenges of applying various RL models. Methods: We performed an extensive search of the following databases: PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Web of Science, Medical Literature Analysis and Retrieval System Online (MEDLINE), and Excerpta Medica Database (EMBASE). Studies published over the past 10 years (2010-2019) that have applied RL for critical care were included. Results: We included 21 papers and found that RL has been used to optimize the choice of medications, drug dosing, and timing of interventions and to target personalized laboratory values. We further compared and contrasted the design of the RL models and the evaluation metrics for each application. Conclusions: RL has great potential for enhancing decision making in critical care. Challenges regarding RL system design, evaluation metrics, and model choice exist. More importantly, further work is required to validate RL in authentic clinical environments. © 2020 Siqi Liu, Kay Choong See, Kee Yuan Ngiam, Leo Anthony Celi, Xingzhi Sun, Mengling Feng.
Source Title: Journal of Medical Internet Research
URI: https://scholarbank.nus.edu.sg/handle/10635/198778
ISSN: 14388871
DOI: 10.2196/18477
Rights: Attribution 4.0 International
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