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Accurate patient outcome estimates after an initial fracture are critical to physicians\u2019 decision-making and patient management. Effective predictions might benefit from analyses of patients\u2019 multimorbidity trajectories and medication usages. If adequately modeled and analyzed, then they could help identify patients at higher risk of recurrent fractures or mortality. Most analytics methods overlook the onset, co-occurrence, and temporal sequence of distinct chronic diseases in the trajectory, and they also seldom consider the combined effects of different medications. To support effective predictions, we develop a novel deep learning\u2013based method that uses a cross-attention mechanism to model patient progression by obtaining \u201ccontextual information\u201d from multimorbidity trajectories. This method also incorporates a nested self-attention network that captures the combined effects of distinct medications by learning the interactions among medications and how dosages might influence post-fracture outcomes. A real-world patient dataset is used to evaluate the proposed method, relative to six benchmark methods. The comparative results indicate that our method consistently outperforms all the benchmarks in precision, recall, F-measures, and area under the curve. The proposed method is generalizable and can be implemented as a decision support system to identify patients at greater risk of recurrent hip fractures or mortality, which should help clinical decision-making and patient management.<\/jats:p>","DOI":"10.1145\/3665250","type":"journal-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T10:01:28Z","timestamp":1715940088000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Mining Multimorbidity Trajectories and Co-Medication Effects from Patient Data to Predict Post\u2013Hip Fracture Outcomes"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4418-1109","authenticated-orcid":false,"given":"Jessica Qiuhua","family":"Sheng","sequence":"first","affiliation":[{"name":"Department of Systems and Operations Management, California State University Northridge, Northridge, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9433-0905","authenticated-orcid":false,"given":"Da","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Marketing, Analytics, and Professional Sales, University of Mississippi, University, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4981-895X","authenticated-orcid":false,"given":"Paul Jen-Hwa","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Operations and Information Systems, University of Utah, Salt Lake City, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8684-1692","authenticated-orcid":false,"given":"Liang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Economics and Management,\u00a0Southwest Jiaotong University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2932-6878","authenticated-orcid":false,"given":"Ting-Shuo","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Surgery, Jen-Ai Hospital, Taichung, Taiwan, Department of Chinese Medicine, Chang Gung University, Taoyuan, Taiwan, and Department of Surgery, Chang Gung Memorial Hospital, Keelung, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,12]]},"reference":[{"issue":"23","key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1056\/NEJM199606063342307","article-title":"Hip fracture","volume":"334","author":"Zuckerman J. 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