{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T13:53:36Z","timestamp":1772027616898,"version":"3.50.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T00:00:00Z","timestamp":1658361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Ministry of Science and Technology of People\u2019s Republic of China","award":["2016YFC1302001"],"award-info":[{"award-number":["2016YFC1302001"]}]},{"DOI":"10.13039\/501100009592","name":"Beijing Municipal Science and Technology Commission","doi-asserted-by":"publisher","award":["D17110000291700"],"award-info":[{"award-number":["D17110000291700"]}],"id":[{"id":"10.13039\/501100009592","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Capital\u2019s Funds for Health Improvement and Research (CFH","award":["2016-1-4031"],"award-info":[{"award-number":["2016-1-4031"]}]},{"name":"National High Level Hospital Clinical Research Funding","award":["2022-GSP-QN-10"],"award-info":[{"award-number":["2022-GSP-QN-10"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Warfarin anticoagulation management requires sequential decision-making to adjust dosages based on patients\u2019 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).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>10\u00a0408 SVR cases with warfarin dosage\u2013response data were retrospectively collected to develop and test an RL algorithm that can continuously recommend daily warfarin doses based on patients\u2019 evolving multidimensional states. The RL algorithm was compared with clinicians\u2019 actual practice and other machine learning and clinical decision rule-based algorithms. The primary outcome was the ratio of patients without in-hospital INRs &amp;gt;3.0 and the INR at discharge within the target range (1.8\u20132.5) (excellent responders). The secondary outcomes were the safety responder ratio (no INRs &amp;gt;3.0) and the target responder ratio (the discharge INR within 1.8\u20132.5).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In the test set (n\u2009=\u20091260), the excellent responder ratio under clinicians\u2019 guidance was significantly lower than the RL algorithm: 41.6% versus 80.8% (relative risk [RR], 0.51; 95% confidence interval [CI], 0.48\u20130.55), also the safety responder ratio: 83.1% versus 99.5% (RR, 0.83; 95% CI, 0.81\u20130.86), and the target responder ratio: 49.7% versus 81.1% (RR, 0.61; 95% CI, 0.58\u20130.65). The RL algorithms performed significantly better than all the other algorithms. Compared with clinicians\u2019 actual practice, the RL-optimized INR trajectory reached and maintained within the target range significantly faster and longer.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>An RL algorithm significantly optimized the post-operation warfarin anticoagulation quality compared with clinicians\u2019 actual practice, suggesting its potential for challenging sequential decision-making tasks.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocac088","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T05:09:15Z","timestamp":1658466555000},"page":"1722-1732","source":"Crossref","is-referenced-by-count":20,"title":["Optimizing the dynamic treatment regime of in-hospital warfarin anticoagulation in patients after surgical valve replacement using reinforcement 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National Center for Cardiovascular Diseases , Beijing, People\u2019s Republic of China"},{"name":"State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases , Beijing, People\u2019s Republic of China"},{"name":"Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing, People\u2019s Republic of China"},{"name":"Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases , Beijing, People\u2019s Republic of China"}]},{"given":"Hongchang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University , Beijing, People\u2019s Republic of China"}]},{"given":"Xiaoting","family":"Su","sequence":"additional","affiliation":[{"name":"National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases , Beijing, People\u2019s Republic of China"},{"name":"State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases , Beijing, People\u2019s Republic of China"},{"name":"Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing, People\u2019s Republic of China"}]},{"given":"Xiaocong","family":"Lian","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University , Beijing, People\u2019s Republic of China"},{"name":"Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University , Beijing, People\u2019s Republic of China"}]},{"given":"Yan","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases , Beijing, People\u2019s Republic of China"},{"name":"State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases , Beijing, People\u2019s Republic of China"}]},{"given":"Xiangyang","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University , Beijing, People\u2019s Republic of China"},{"name":"Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University , Beijing, People\u2019s Republic of China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9162-6492","authenticated-orcid":false,"given":"Zhe","family":"Zheng","sequence":"additional","affiliation":[{"name":"National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases , Beijing, People\u2019s Republic of China"},{"name":"State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases , Beijing, People\u2019s Republic of China"},{"name":"Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing, People\u2019s Republic of China"},{"name":"Department of Cardiovascular Surgery, 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