Interpretable machine learning approach for optimizing hospice care predictions using health assessment data
ML models were developed using health assessment data to explore their potential in guiding the selection of hospice care services for end-of-life patients. The findings demonstrate a data-driven approach that may support more informed and personalized clinical decisions, while representing an initial proof of concept for integrating ML into hospice care planning.
Authors
- Peng‐Chan Lin (ORCID: https://orcid.org/0000-0002-9424-1985)
- Wei‐Shu Lai (ORCID: https://orcid.org/0000-0002-8308-9228)
- Jui-Hung Tsai (ORCID: https://orcid.org/0000-0003-0318-3474)
- Sung In Cho (ORCID: https://orcid.org/0000-0003-4251-7131)
- Hsin-Hung Chou (ORCID: https://orcid.org/0000-0003-1627-9598)
Institutions
- National Chi Nan University (TW)
- National Cheng Kung University Hospital (TW)
- National Cheng Kung University (TW)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-11-28
- DOI
- https://doi.org/10.1186/s12911-025-03289-w
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00