Machine learning for predicting extended length of stay in elderly patients with hip fractures: An enhanced recovery after surgery perspective
Both statistical and ML approaches identified delayed surgery and pneumonia as critical eLOS predictors, while ML revealed complex interactions involving coagulation dynamics and reinforced TXA's protective role. These findings support ML-augmented ERAS protocols targeting modifiable risk factors. External validation and clinical implementation studies are needed to confirm utility in routine practice.
Authors
- Chaoyang Qu
- Fuqiang Tan (ORCID: https://orcid.org/0000-0002-0194-958X)
- Bin Li (ORCID: https://orcid.org/0000-0002-7088-8274)
- Xin Shu (ORCID: https://orcid.org/0000-0001-5626-8645)
- Peng Xu (ORCID: https://orcid.org/0000-0002-1440-6509)
- Haibo Pu
Institutions
- People's Hospital of Bishan District (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- First People's Hospital of Chongqing (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251406311
- Primary Topic
- Enhanced Recovery After Surgery
- Type
- article
- Field-Weighted Citation Impact
- 0.00