Development and Temporal External Validation of a Parsimonious, Interpretable Machine Learning Model for Predicting 6-Month Mortality in Long-Term Care Facilities: A Retrospective Cohort Study
Yun-Cheng Tsai, Yi-Lin Wu, Yung-Chen Yu, Yi-Wen Chen, Yi-Wen Chen, Yi-Ching Yang, Yu-Tai Lo · 2026
Read Development and Temporal External Validation of a Parsimonious, Interpretable Machine Learning Model for Predicting 6-Month Mortality in Long-Term Care Facilities: A Retrospective Cohort Study by Yun-Cheng Tsai, Yi-Lin Wu, Yung-Chen Yu, Yi-Wen Chen, Yi-Wen Chen, Yi-Ching Yang, Yu-Tai Lo (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13581260.txt.
Description
Tsai and colleagues built in 2026 a machine learning model that predicts six-month death risk for new residents of long-term care facilities in Taiwan. They used routine intake data from a nationwide registry covering hundreds of facilities. A hybrid model combining gradient boosting and random forest performed best among eight algorithms. It kept strong accuracy in a later, separate validation year.
Explanation tools showed frequent recent hospital stays, poor daily-living ability, and weight loss drove the predictions. Performance held steady across age and sex groups. The authors offer the tool as a simple, interpretable aid for care planning at admission, without needing hospital record links.
Catalog record
| CREATOR | Yun-Cheng Tsai, Yi-Lin Wu, Yung-Chen Yu, Yi-Wen Chen, Yi-Wen Chen, Yi-Ching Yang, Yu-Tai Lo |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 73 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |