Development and validation of an early prediction model for bee-sting-induced acute kidney injury using machine learning
Yiqin Fang, Juan Zhang, Zhonghua Hu, Xiaoling Yu, Yiyan Xie, Yueming Liu, Huizhen Wu · 2026
Read Development and validation of an early prediction model for bee-sting-induced acute kidney injury using machine learning by Yiqin Fang, Juan Zhang, Zhonghua Hu, Xiaoling Yu, Yiyan Xie, Yueming Liu, Huizhen Wu (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-13202659.txt.
Description
Bee stings can injure kidneys before standard blood tests notice, and a 2026 study by Yiqin Fang builds an early warning model from machine learning. The retrospective study uses records of 305 bee-sting patients admitted between 2018 and 2025, of whom about a third developed acute kidney injury.
From thirty-three admission variables, a selection method kept eight predictors, including dark urine, lactate, and creatinine. Six algorithms were trained and compared, and the gradient boosting model performed best on the held-out test data in the authors' evaluation. An explanation method ranked the strongest predictors, and the team converted the model into a bedside nomogram for timely risk decisions.
Catalog record
| CREATOR | Yiqin Fang, Juan Zhang, Zhonghua Hu, Xiaoling Yu, Yiyan Xie, Yueming Liu, Huizhen Wu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 36 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |