Establishment of a postoperative delirium risk prediction model for elderly hip fracture patients based on machine learning algorithms
Yayun Xing, Yuetong Wang, Yuejiao Huang, Fanguo Lin, Yuye Zhang · 2025
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Description
Yayun Xing and co-authors build a machine learning model to flag delirium risk after hip surgery, in this 2025 study. Older patients who break a hip often develop delirium after the operation, a confusion that harms recovery. No sure treatment exists, but many cases can be prevented if high-risk patients are spotted early.
The team studied eight hundred one patients, of whom about a quarter developed delirium. Six learning methods were trained and checked with cross-validation. A boosting method called CatBoost won, with strong scores on both test and validation sets. Its key warning signs spanned oxygen levels around the operation, blood sugar, fasting time, a nutrition protein, pain scores, and kidney markers. The authors say the model can give each patient a personal risk estimate to guide preventive care.
Catalog record
| CREATOR | Yayun Xing, Yuetong Wang, Yuejiao Huang, Fanguo Lin, Yuye Zhang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |