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
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.
Authors
- Aimin Yang (ORCID: https://orcid.org/0000-0003-2968-1762)
- Mai Shi (ORCID: https://orcid.org/0000-0002-2798-3268)
- Andrea O. Y. Luk (ORCID: https://orcid.org/0000-0002-5244-6069)
- Raymond Wong (ORCID: https://orcid.org/0000-0002-9011-4268)
- Elaine Chow (ORCID: https://orcid.org/0000-0002-4147-3387)
- Alice P.S. Kong (ORCID: https://orcid.org/0000-0001-8927-6764)
- Jiazhou Yu (ORCID: https://orcid.org/0000-0002-9249-2721)
- Eric S. H. Lau (ORCID: https://orcid.org/0000-0003-1581-5643)
- Juliana C.N. Chan (ORCID: https://orcid.org/0000-0003-1325-1194)
- Ronald C.W. (ORCID: https://orcid.org/0000-0002-1227-803X)
- Jones C. M. Chan
Institutions
- Chinese University of Hong Kong (HK)
- Prince of Wales Hospital (CN)
Publication Details
- Journal
- Age and Ageing
- Published
- 2025-08-29
- DOI
- https://doi.org/10.1093/ageing/afaf285
- Primary Topic
- Chronic Disease Management Strategies
- Type
- article
- Field-Weighted Citation Impact
- 0.00