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...

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Detalles Bibliográficos
Publicado en:	Age & Ageing Vol. 54; no. 10; pp. 1 - 13
Autores principales:	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
Formato:	Artículo
Publicado:	Oxford University Press / USA Oct2025
Materias:	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
Acceso en línea:	Ver este registro en EBSCOhost