A machine learning-based model for predicting the postoperative risk of acute kidney injury in neonates
Liping He, Tianyin Gao, Yanli Tang, Saifen Jin, Manli Zhuang · 2025
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Description
Liping He and co-authors build a machine learning model that predicts which newborns will suffer kidney injury after surgery, in a 2025 study. Kidney injury is a serious complication in hospitalized newborns and is easy to miss early. The team gathered records for 2,025 neonates and split them into training and test sets.
Twenty-five variables fed the models, from age and operation length to fluids given and output after surgery. Six algorithms competed, and a classic logistic regression won, with the best balance of catching cases and not over-calling them. About 5.4 percent of the babies developed the injury. Age, operation duration, and urine output carried the most weight in the predictions. An explanation tool then showed, baby by baby, how the winning model reached each call.
Catalog record
| CREATOR | Liping He, Tianyin Gao, Yanli Tang, Saifen Jin, Manli Zhuang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |