{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T07:52:29Z","timestamp":1793087549050,"version":"4.5.0"},"reference-count":69,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a post-hoc analysis from the Randomized Evaluation of Long-Term Anticoagulant Therapy trial database. One traditional prediction method, logistic regression (LGR), and four ML techniques\u2014naive Bayes, random forest (RF), classification and regression tree, and extreme gradient boosting (XGBoost)\u2014were combined to construct our scheme. Area under the receiver operating characteristic curve (AUC) of RF (0.780) and XGBoost (0.717) was higher than that of LGR (0.674) in predicting vascular events. In predicting bleeding, AUC of RF (0.684) and XGBoost (0.618) showed higher values than those generated by LGR (0.605). Our integrated ML feature selection scheme based on the two convincing prediction techniques identified age, history of congestive heart failure and myocardial infarction, smoking, kidney function, and body mass index as major variables of vascular events; age, kidney function, smoking, bleeding history, concomitant use of specific drugs, and dabigatran dosage as major variables of bleeding. ML is an effective data analysis algorithm for solving complex medical data. Our results may provide preliminary direction for precision medicine.<\/jats:p>","DOI":"10.3390\/jpm12050756","type":"journal-article","created":{"date-parts":[[2022,5,8]],"date-time":"2022-05-08T21:31:20Z","timestamp":1652045480000},"page":"756","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme\u2014A Post Hoc Analysis"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5171-2911","authenticated-orcid":false,"given":"Yung-Chuan","family":"Huang","sequence":"first","affiliation":[{"name":"Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan"},{"name":"Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Chen","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mao-Jhen","family":"Jhou","sequence":"additional","affiliation":[{"name":"Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8278-0033","authenticated-orcid":false,"given":"Mingchih","family":"Chen","sequence":"additional","affiliation":[{"name":"Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan"},{"name":"Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7911-2253","authenticated-orcid":false,"given":"Chi-Jie","family":"Lu","sequence":"additional","affiliation":[{"name":"Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan"},{"name":"Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan"},{"name":"Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/S1474-4422(21)00252-0","article-title":"Global, regional, and national burden of stroke and its risk factors, 1990\u20132019: A systematic analysis for the Global Burden of Disease Study 2019","volume":"20","author":"Feigin","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2370","DOI":"10.1001\/jama.285.18.2370","article-title":"Prevalence of diagnosed atrial fibrillation in adults: National implications for rhythm management and stroke prevention: The An Ticoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study","volume":"285","author":"Go","year":"2001","journal-title":"JAMA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2893","DOI":"10.1093\/eurheartj\/ehw210","article-title":"ESC Scientific Document Group 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS","volume":"37","author":"Kirchhof","year":"2016","journal-title":"Eur. 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