ReviewJournal of medical Internet research2020
Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review.
Review in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
77 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.Journal of pain and symptom management · 2024Pooled it
- Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review.Journal of neuroengineering and rehabilitation · 2022Pooled it
- Review
- Reinforcement Learning for Intraoperative Hypotension Management with Consideration to Postoperative Acute Kidney Injury.Kidney360 · 2026Article
- HRRT: hierarchical reinforcement learning for renal replacement therapy decision support.NPJ digital medicine · 2026Article
- Behavior-aware deep reinforcement learning for multi-objective outpatient scheduling optimization.Scientific reports · 2026Article
- A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Healthcare Administration and Clinical Informatics: A Critical Review and Governance Roadmap.Healthcare (Basel, Switzerland) · 2026Review
- Current Approaches to the Management of Acute Surgical and Medical Emergencies: A Structured Narrative Review.Cureus · 2026Review
- Treatment Response Phenotyping Informed by Patient Physiologic Characteristics Could Drive Precision Critical Care Through Augmented Intelligence: A Narrative Review.CHEST critical care · 2026Article
- Participatory-informed preference optimization (PiPrO): A reinforcement learning simulation study.PLOS digital health · 2026Article
- rECMOmender: Reinforcement Learning for Decision Support in Venovenous Extracorporeal Membrane Oxygenation Management.Critical care explorations · 2026Article
- Smart Imitator: Learning from Imperfect Clinical Decisions.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Preventing relapse in ulcerative colitis remission maintenance: machine learning prediction and reinforcement-learning herbal prescription optimization with external validation.Frontiers in medicine · 2026Article
- Beyond antimicrobials: a host-directed precision pharmacotherapy framework for septic shock.Frontiers in medicine · 2026Article
- A reinforcement learning algorithm to optimize resource utilization in combat casualty care.Scientific reports · 2025Article
- Reinforcement Learning to Prevent Acute Care Events Among Medicaid Populations: Mixed Methods Study.JMIR AI · 2025Article
- Genetic Artificial Intelligence in Gastrointestinal Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes.Healthcare (Basel, Switzerland) · 2025Review
17 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDecision 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.
objectiveThis 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.
methodsWe 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.
resultsWe 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.
conclusionsRL 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.