Machine learning prediction of survival in centenarians after age 100: a retrospective, population-based cohort study
Utilizing ML models and routinely collected EHRs can predict short-term survival in centenarians with moderate accuracy. Further research is needed to determine whether mortality predictors differ across age in the oldest-old population.
Authors
- Ching‐Lung Cheung (ORCID: https://orcid.org/0000-0002-6233-9144)
- Jacqueline Kwan Yuk Yuen (ORCID: https://orcid.org/0000-0002-9682-4367)
- Kathryn Choon Beng Tan (ORCID: https://orcid.org/0000-0001-9037-0416)
- Jonathan K. L. Mak (ORCID: https://orcid.org/0000-0003-4454-8580)
- Noel C. Yue
- Gloria Hoi‐Yee Li (ORCID: https://orcid.org/0000-0003-0275-2356)
- Tung Wai Auyeung
Institutions
- Hong Kong Polytechnic University (HK)
- Chinese University of Hong Kong (HK)
- Karolinska Institutet (SE)
- Hong Kong Jockey Club (HK)
- Hebrew SeniorLife (US)
- University of Hong Kong (HK)
Publication Details
- Journal
- The Journals of Gerontology Series A
- Published
- 2025-10-02
- DOI
- https://doi.org/10.1093/gerona/glaf218
- Primary Topic
- Frailty in Older Adults
- Type
- article
- Field-Weighted Citation Impact
- 0.00