{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T16:28:18Z","timestamp":1791822498665,"version":"4.3.4"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":41,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Chinese University of Hong Kong (CUHK) Impact-Research-Fellowship Scheme"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Older adults face high risk of falls. We developed an electronic-health-record (EHR) based machine-learning (ML) model to predict 1-year risk of fall in older adults for pre-emptive intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We included 4\u00a0902\u00a0161 records from 1\u00a0142\u00a0000 adults aged \u226565\u00a0years who attended the Hong Kong Hospital Authority (HA) facilities in 2013\u20132017. We included 260 predictors including demographics, in-patient\/out-patient admissions, emergency department (ED) attendance, complications, medications and laboratory tests during 1-year period to predict fall events based on diagnostic codes in the ensuing 12\u00a0months. The cohort was randomly split into training, testing and internal validation sets in a 7:2:1 ratio. We evaluated the performance of six ML-algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>67\u00a0163 fall events were accrued with the XGBoost model having the best performance in the validation set (area-under-the-receiver-operating-characteristic-curve [AUROC]\u2009=\u20090.979, area-under-the-precision-recall-curve [AUPRC]\u2009=\u20090.764; positive-predictive-value [PPV]\u2009=\u20090.614) versus logistic-regression model (AUROC\u2009=\u20090.885, AUPRC\u2009=\u20090.169; PPV\u2009=\u20090.210). The top 30 predictors included number of ED attendance, fasting plasma glucose, number and types of outpatient appointments, ED triage category of \u2018urgent\u2019, number of admissions and stay, age, residential districts, history of fall and medication use with an AUROC of 0.939 in a validation cohort of patients with diabetes. In an age- and sex-matched sub-cohort, compared to the widely-used Morse Fall Score, XGBoost model had higher sensitivity (0.569-versus-0.139) with optimal balance of identifying positive cases whilst simultaneously minimising false positives and false negative (F1 score: 0.626-versus-0.555).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our ML-model highlights the utility of EHR in identifying high-risk individuals for falls, supporting integrating into the EHR system for targeted preventive actions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ageing\/afaf285","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T12:07:04Z","timestamp":1758802024000},"source":"Crossref","is-referenced-by-count":6,"title":["A novel electronic-health-record based, machine-learning model to predict 1-year risk of fall hospitalisation in older adults: a Hong Kong territory-wide cohort and modelling study"],"prefix":"10.1093","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2968-1762","authenticated-orcid":false,"given":"Aimin","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mai","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric S H","family":"Lau","sequence":"additional","affiliation":[{"name":"Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiazhou","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea O Y","family":"Luk","sequence":"additional","affiliation":[{"name":"Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald C W","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alice P S","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jones C M","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juliana C N","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elaine","family":"Chow","sequence":"additional","affiliation":[{"name":"Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]},{"name":"Phase 1 Clinical Trial Centre, The Chinese University of Hong Kong, Prince of Wales Hospital , Hong Kong Special Administrative Region ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"key":"2025100913541815100_ref1","volume-title":"WHO Global Report on Falls Prevention in Older Age","author":"Ageing WHO, Unit LC","year":"2008"},{"key":"2025100913541815100_ref2","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1001\/jama.2023.3054","article-title":"Trends in deaths from falls among adults aged 65 years or older in the US, 1999-2020","volume":"329","author":"Santos-Lozada","year":"2023","journal-title":"JAMA."},{"key":"2025100913541815100_ref3","doi-asserted-by":"crossref","DOI":"10.18356\/9789210028233","volume-title":"World Social Report 2023: Leaving No One behind in an Ageing 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