A machine learning model for predicting postoperative complication risk in young and middle-aged patients with femoral neck fractures
This study successfully developed and validated a logistic regression-based predictive model, augmented with SHAP explanations, providing an effective tool for assessing the risk of postoperative complications in young and middle-aged patients with femoral neck fractures.
Authors
- Dongze Lin (ORCID: https://orcid.org/0000-0002-2095-7665)
- Fengfei Lin (ORCID: https://orcid.org/0000-0002-4360-0386)
- Xiaoxing Jiang (ORCID: https://orcid.org/0000-0002-4884-2683)
- Bin Chen (ORCID: https://orcid.org/0000-0002-2882-9756)
- Yufen Huang (ORCID: https://orcid.org/0000-0002-0588-6627)
- Shanglin Shangguan
Institutions
- Fujian University of Traditional Chinese Medicine (CN)
- The 180th Hospital of PLA (CN)
- Fuzhou Second Hospital (CN)
Publication Details
- Journal
- Frontiers in Surgery
- Published
- 2025-08-26
- DOI
- https://doi.org/10.3389/fsurg.2025.1591671
- Primary Topic
- Hip and Femur Fractures
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Fujian Province
- Fujian Provincial Health Commission
- Medical Innovation Project of Fujian Province