Development and validation of an interpretable machine learning model for predicting intrinsic capacity decline in elderly inpatients
The Support Vector Machine model can effectively identify high-risk older hospitalized patients with decreased intrinsic capacity. Early risk identification and targeted intervention measures applied in clinical practice are helpful to improve the health level of older hospitalized patients and achieve healthy aging.
Authors
- Hongxia Lu (ORCID: https://orcid.org/0000-0002-9956-5591)
- Ying Tang (ORCID: https://orcid.org/0000-0003-2665-7812)
- Wenyuan Li (ORCID: https://orcid.org/0000-0002-5113-7280)
- Junbo Chen (ORCID: https://orcid.org/0000-0002-1623-6339)
- Tian Gao (ORCID: https://orcid.org/0009-0004-0557-4342)
- Han Lin (ORCID: https://orcid.org/0000-0002-3446-5098)
- Yuxia Ma (ORCID: https://orcid.org/0009-0007-2394-3430)
- Jiang Nan
- Jialu Li (ORCID: https://orcid.org/0009-0003-8276-9664)
Institutions
- The People's Hospital of LinXia (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Experimental Gerontology
- Published
- 2025-10-15
- DOI
- https://doi.org/10.1016/j.exger.2025.112923
- Primary Topic
- Health and Well-being Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00