ArticleJournal of the American Medical Informatics Association : JAMIA2022
Optimizing the dynamic treatment regime of in-hospital warfarin anticoagulation in patients after surgical valve replacement using reinforcement learning.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Efficacy, Safety, and Cost-Effectiveness of "Internet + Pharmacy Care" Via the Alfalfa App in Warfarin Therapy Management After Cardiac Valve Replacement: Randomized Controlled Trial.JMIR mHealth and uHealth · 2025Trial
- Personalized multi-agent reinforcement learning framework for adaptive chronic disease therapy management.Scientific reports · 2026Article
- AI-WAR: a novel warfarin management software with a bidirectional LSTM dosing model improves time in therapeutic range.Frontiers in pharmacology · 2026Article
- Machine Learning for Warfarin Therapy: A Systematic Review.Pharmaceuticals (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
- Chronic Kidney Disease-Mineral and Bone Disorder Management in 4D: The Case for Dynamic Treatment Regime Methods to Optimize Care.Current osteoporosis reports · 2025Review
- Cardiac Surgery-Specific Subtle Perioperative Serum Creatinine Change in Defining Acute Kidney Injury After Coronary Surgery.JACC. Advances · 2024Article
- Individualized decision making in on-scene resuscitation time for out-of-hospital cardiac arrest using reinforcement learning.NPJ digital medicine · 2024Article
- Prediction of coronary artery disease based on facial temperature information captured by non-contact infrared thermography.BMJ health & care informatics · 2024Article
- Personalizing renal replacement therapy initiation in the intensive care unit: a reinforcement learning-based strategy with external validation on the AKIKI randomized controlled trials.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Application of eHealth Tools in Anticoagulation Management After Cardiac Valve Replacement: Scoping Review Coupled With Bibliometric Analysis.JMIR mHealth and uHealth · 2024Article
- A Mobile Application for Anticoagulation Management in Patients After Heart Valve Replacement: A Usability Study.Patient preference and adherence · 2024Article
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9 authors.
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Abstract
objectiveWarfarin anticoagulation management requires sequential decision-making to adjust dosages based on patients' evolving states continuously. We aimed to leverage reinforcement learning (RL) to optimize the dynamic in-hospital warfarin dosing in patients after surgical valve replacement (SVR). MATERIALS AND
methods10 408 SVR cases with warfarin dosage-response data were retrospectively collected to develop and test an RL algorithm that can continuously recommend daily warfarin doses based on patients' evolving multidimensional states. The RL algorithm was compared with clinicians' actual practice and other machine learning and clinical decision rule-based algorithms. The primary outcome was the ratio of patients without in-hospital INRs >3.0 and the INR at discharge within the target range (1.8-2.5) (excellent responders). The secondary outcomes were the safety responder ratio (no INRs >3.0) and the target responder ratio (the discharge INR within 1.8-2.5).
resultsIn the test set (n = 1260), the excellent responder ratio under clinicians' guidance was significantly lower than the RL algorithm: 41.6% versus 80.8% (relative risk [RR], 0.51; 95% confidence interval [CI], 0.48-0.55), also the safety responder ratio: 83.1% versus 99.5% (RR, 0.83; 95% CI, 0.81-0.86), and the target responder ratio: 49.7% versus 81.1% (RR, 0.61; 95% CI, 0.58-0.65). The RL algorithms performed significantly better than all the other algorithms. Compared with clinicians' actual practice, the RL-optimized INR trajectory reached and maintained within the target range significantly faster and longer. DISCUSSION: RL could offer interactive, practical clinical decision support for sequential decision-making tasks and is potentially adaptable for varied clinical scenarios. Prospective validation is needed.
conclusionAn RL algorithm significantly optimized the post-operation warfarin anticoagulation quality compared with clinicians' actual practice, suggesting its potential for challenging sequential decision-making tasks.
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