An Explainable Machine Learning Model to Predict Acute Kidney Injury After Cardiac Surgery: A Retrospective Cohort Study
Yuchen Gao, Chunrong Wang, Wenhao Dong, Bianfang Li, Jianhui Wang, Jun Li, Yu Tian, Jia Liu, Yuefu Wang · 2023
Read An Explainable Machine Learning Model to Predict Acute Kidney Injury After Cardiac Surgery: A Retrospective Cohort Study by Yuchen Gao, Chunrong Wang, Wenhao Dong, Bianfang Li, Jianhui Wang, Jun Li, Yu Tian, Jia Liu, Yuefu Wang (2023) 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-10706584.txt.
Description
Gao and eight co-authors build a machine learning model to predict kidney injury after heart surgery in a 2023 paper. The study used records from a large Chinese heart hospital over two years. About a third of patients developed the injury.
Patient data, lab values, and operation details fed two models. A gradient boosting model sorted patients more accurately than a regression model on held-out data. Two measures stood out as top predictors: kidney function level and a blood marker on arrival at intensive care. A visual method showed how the top features pushed each prediction. The authors say such models can guide preventive care for high-risk patients.
Catalog record
| CREATOR | Yuchen Gao, Chunrong Wang, Wenhao Dong, Bianfang Li, Jianhui Wang, Jun Li, Yu Tian, Jia Liu, Yuefu Wang |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 40 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |