Development and validation of a machine learning-based risk prediction model for postoperative delirium in older patients with hip fracture
An ML-based model was developed and validated to predict postoperative delirium risk in older patients with hip fracture. These findings may help to develop personalized interventions to provide better treatment plans and optimal resource allocation. The interpretable framework can increase the transparency of the model and facilitate understanding the reliability of the predictive model for the physicians.
Authors
- Yuan Gao (ORCID: https://orcid.org/0000-0002-2990-9205)
- Qingqing Su (ORCID: https://orcid.org/0000-0003-1290-8094)
- Xiaojie Fu (ORCID: https://orcid.org/0000-0002-7367-2735)
- Mi Kyung Song (ORCID: https://orcid.org/0000-0003-3799-0426)
- Weili Zhang (ORCID: https://orcid.org/0000-0002-1862-1492)
- Jie Song (ORCID: https://orcid.org/0000-0002-9559-0634)
- Nan Tang (ORCID: https://orcid.org/0000-0001-7102-7209)
Institutions
- Chinese PLA General Hospital (CN)
- First Affiliated Hospital of Chinese PLA General Hospital (CN)
Publication Details
- Journal
- The Journals of Gerontology Series A
- Published
- 2025-09-15
- DOI
- https://doi.org/10.1093/gerona/glaf200
- Citations
- 2
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 3.09