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.
Objective 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. Methods We included 4 902 161 records from 1 142 000 adults aged ≥65 years who attended the Ho...
| Published in: | Age & Ageing Vol. 54; no. 10; pp. 1 - 13 |
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| Main Authors: | , , , , , , , , , , |
| Format: | Article |
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Oxford University Press / USA
Oct2025
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=189289160&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 189289160 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00020729 AGA jtl: Age & Ageing issn: 00020729 maglogo: N pubinfo: dt: Oct2025 vid: 54 iid: 10 pid: 622 pub: Oxford University Press / USA artinfo: ui: 189289160 10.1093/ageing/afaf285 ppf: 1 ppct: 12 formats: tig: atl: 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. aug: au: Yang, Aimin Shi, Mai Lau, Eric S H Yu, Jiazhou Luk, Andrea O Y Ma, Ronald C W Kong, Alice P S Wong, Raymond Chan, Jones C M Chan, Juliana C N Chow, Elaine affil: Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China Phase 1 Clinical Trial Centre, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China su: Hong Kong (China) Patients Hospital admission & discharge Residential patterns Hospitals Retrospective studies Age distribution Case-control method Hospital care of older people Fasting Old age Blood sugar analysis Predictive tests Random forest algorithms Early medical intervention Receiver operating characteristic curves Outpatient services in hospitals Logistic regression analysis Probability theory Questionnaires Diagnostic errors Descriptive statistics Hospital emergency services Electronic health records Medical records Acquisition of data Medical appointments Morse Fall Scale Machine learning Accuracy Calibration Accidental falls Sensitivity & specificity (Statistics) Algorithms Nosology Medical triage Drug utilization Evaluation sug: subj: Patients Hospital admission & discharge Residential patterns Hospitals Retrospective studies Age distribution Case-control method Hospital care of older people Fasting Old age Hong Kong (China) General Medical and Surgical Hospitals General (except paediatric) hospitals All Other Outpatient Care Centers Blood sugar analysis Predictive tests Random forest algorithms Early medical intervention Receiver operating characteristic curves Outpatient services in hospitals Logistic regression analysis Probability theory Questionnaires Diagnostic errors Descriptive statistics Hospital emergency services Electronic health records Medical records Acquisition of data Medical appointments Morse Fall Scale Machine learning Accuracy Calibration Accidental falls Sensitivity & specificity (Statistics) Algorithms Nosology Medical triage Drug utilization Evaluation keyword: adult area under the roc curve diabetes mellitus electric health records electronic medical records fall fall risks hong kong machine learning mental recall older adult older adults outpatients precision risk prediction type 2 adult area under the roc curve diabetes mellitus electric health records electronic medical records fall fall risks hong kong machine learning mental recall older adult older adults outpatients precision risk prediction type 2 ab: Objective 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. Methods We included 4 902 161 records from 1 142 000 adults aged ≥65 years who attended the Hong Kong Hospital Authority (HA) facilities in 2013–2017. 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 months. 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. Results 67 163 fall events were accrued with the XGBoost model having the best performance in the validation set (area-under-the-receiver-operating-characteristic-curve [AUROC] = 0.979, area-under-the-precision-recall-curve [AUPRC] = 0.764; positive-predictive-value [PPV] = 0.614) versus logistic-regression model (AUROC = 0.885, AUPRC = 0.169; PPV = 0.210). The top 30 predictors included number of ED attendance, fasting plasma glucose, number and types of outpatient appointments, ED triage category of 'urgent', 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). Conclusions 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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