Profiling unmet post–acute care needs of an inpatient population in Hong Kong: can real-world data and machine learning algorithms bring precision to tertiary prevention in the community?
These findings support a precision-driven approach to designing rehospitalisation prevention programmes that target individuals aged 50 to 64 years discharged with specific clinical profiles, and developing and allocating human capital for these targeted prevention programmes.
Authors
- Calvin Law (ORCID: https://orcid.org/0000-0002-6407-0871)
- Eman Leung (ORCID: https://orcid.org/0000-0002-8267-1785)
- Olivia Lam
- Frank Chen (ORCID: https://orcid.org/0000-0003-4707-9361)
- Jingjing Guan (ORCID: https://orcid.org/0000-0002-9911-2293)
- Y.-J. LIU (ORCID: https://orcid.org/0009-0006-2706-4014)
- Martin C. S. Wong (ORCID: https://orcid.org/0000-0001-7706-9370)
- Chi Tim Hung (ORCID: https://orcid.org/0000-0003-2103-8377)
- Hector W. H. Tsang (ORCID: https://orcid.org/0000-0003-3003-8598)
- YF Wong
- Yinan He (ORCID: https://orcid.org/0000-0002-6200-9938)
- Albert Lee (ORCID: https://orcid.org/0000-0002-5888-5660)
- EK Yeoh
- Yat Chow
- NY Chan
- Sam C. C. Ching
- Sarah TY Yau
Institutions
- Hong Kong Polytechnic University (HK)
- City University of Hong Kong (HK)
- Chinese University of Hong Kong (HK)
- Princess Margaret Hospital (NZ)
- Princess Margaret Hospital (HK)
- Epigenomics (Germany) (DE)
- Saft (France) (FR)
Publication Details
- Journal
- Hong Kong Medical Journal
- Published
- 2025-12-19
- DOI
- https://doi.org/10.12809/hkmj2411474
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00