Development of an interpretable machine learning model for frailty risk prediction in older adult care institutions: a mixed-methods, cross-sectional study in China
This novel, interpretable ML-based frailty risk prediction model can enhance decision-making in the care of older adults by providing transparent predictions and identifying crucial factors associated with frailty. It establishes a foundation for targeted management and broader ML applications in healthcare systems, such as IMOACIs, particularly in developing regions.
Authors
- Qing Peng (ORCID: https://orcid.org/0000-0003-3060-8685)
- Hua Peng (ORCID: https://orcid.org/0000-0001-5583-537X)
- Weizi Wu (ORCID: https://orcid.org/0000-0002-1885-7198)
- Liqing Yue (ORCID: https://orcid.org/0000-0001-7349-3588)
- H J Zhong (ORCID: https://orcid.org/0009-0005-5433-8909)
- Jing Li (ORCID: https://orcid.org/0000-0002-2449-1276)
- Weihong Huang (ORCID: https://orcid.org/0000-0001-6206-9909)
- L.F. Lv
- Zeng Shumei
Institutions
- Central South University (CN)
- China Mobile (China) (CN)
- Yale University (US)
- Maternal and Child Health Hospital of Sichuan Province (CN)
- Inner Mongolia People's Hospital (CN)
- Xiangya Hospital Central South University (CN)
Publication Details
- Journal
- BMJ Open
- Published
- 2025-07-01
- DOI
- https://doi.org/10.1136/bmjopen-2024-095460
- Citations
- 4
- Primary Topic
- Frailty in Older Adults
- Type
- article
- Field-Weighted Citation Impact
- 4.90