Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach
Lingyu Xu, Siqi Jiang, Chenyu Li, Xue Gao, Chen Guan, Tianyang Li, Ningxin Zhang, Shuang Gao, Xinyuan Wang, Yanfei Wang, Lin Che, Yan Xu · 2024
Read Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach by Lingyu Xu, Siqi Jiang, Chenyu Li, Xue Gao, Chen Guan, Tianyang Li, Ningxin Zhang, Shuang Gao, Xinyuan Wang, Yanfei Wang, Lin Che, Yan Xu (2024) 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-11648138.txt.
Description
Lingyu Xu and co-authors build in 2024 a machine learning tool that predicts acute kidney injury and acute kidney disease in hospitalized children. Both conditions are common in this group and raise death risk and stay length. Early warning would let clinicians act sooner. They trained ten algorithms on records from 2,346 children admitted between 2020 and 2023.
Injury struck about fifteen percent and disease about sixteen percent of patients. Children with both had the highest death rate, near seven percent. One gradient boosting model performed best for both outcomes. Explanation analysis named blood markers and drug exposure among top predictors. An online tool was released.
Catalog record
| CREATOR | Lingyu Xu, Siqi Jiang, Chenyu Li, Xue Gao, Chen Guan, Tianyang Li, Ningxin Zhang, Shuang Gao, Xinyuan Wang, Yanfei Wang, Lin Che, Yan Xu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |