当前位置： SCI文献检索 > JOURNAL OF MEDICAL INTERNET RESEARCH期刊下所有文献 > Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review.

Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review.

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.

journal_name

J Med Internet Res

journal_title

Journal of medical Internet research

authors

Liu S,See KC,Ngiam KY,Celi LA,Sun X,Feng M

doi

10.2196/18477

subject

Has Abstract

pub_date

2020-07-20 00:00:00

pages

e18477

issue

7

eissn

1439-4456

issn

1438-8871

pii

v22i7e18477

journal_volume

22

pub_type

杂志文章,评审

相关文献

JOURNAL OF MEDICAL INTERNET RESEARCH文献大全