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Hospital readmission causes $26 billion preventable expenses to the U.S. health systems annually and often indicates suboptimal patient care. To alleviate those severe financial and health consequences, it is crucial to proactively predict patients\u2019 readmission risk. Such prediction is challenging because the evolution of patients\u2019 medical history is dynamic and complex. The state-of-the-art studies apply statistical models which use static predictors in a period, failing to consider patients\u2019 heterogeneous medical history. Our approach \u2013\n            <jats:bold>Trajectory-BAsed DEep Learning (TADEL)<\/jats:bold>\n            \u2013 is motivated to tackle the deficiencies of the existing approaches by capturing dynamic medical history. We evaluate TADEL on a five-year national Medicare claims dataset including 3.6 million patients per year over all hospitals in the United States, reaching an F1 score of 87.3% and an AUC of 88.4%. Our approach significantly outperforms all the state-of-the-art methods. Our findings suggest that health status factors and insurance coverage are important predictors for readmission. This study contributes to IS literature and analytical methodology by formulating the trajectory-based readmission prediction problem and developing a novel deep-learning-based readmission risk prediction framework. From a health IT perspective, this research delivers implementable methods to assess patients\u2019 readmission risk and take early interventions to avoid potential negative consequences.\n          <\/jats:p>","DOI":"10.1145\/3468780","type":"journal-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T01:18:12Z","timestamp":1634606292000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Readmission Prediction for Patients with Heterogeneous Medical History: A Trajectory-Based Deep Learning Approach"],"prefix":"10.1145","volume":"13","author":[{"given":"Jiaheng","family":"Xie","sequence":"first","affiliation":[{"name":"Lerner College of Business &amp; Economics, University of Delaware, Newark, DE, USA"}]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Eller College of Management, University of Arizona, Tucson, AZ, USA"}]},{"given":"Jian","family":"Ma","sequence":"additional","affiliation":[{"name":"University of Colorado, Colorado Springs, Colorado Springs CO, USA"}]},{"given":"Daniel","family":"Zeng","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Jenny","family":"Lo-Ciganic","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Outcomes &amp; Policy, University of Florida, FL"}]}],"member":"320","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2011.72"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamcollsurg.2013.01.008"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1287\/isre.2014.0553"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3097997"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1201268"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sste.2016.03.001"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000006746"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1111\/hiv.12509"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocv110"},{"key":"e_1_3_2_11_2","volume-title":"Performance of the Massachusetts Health Care System Series: A Focus on Provider Quality","year":"2015","unstructured":"Center for Health Information and Analysis. 2015. 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