Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation
ML models can serve as reliable tools for forecasting AKI in the critically ill population with CHD. The XGBoost model is highly accurate and may aid doctors in identifying high-risk individuals for early intervention to lower mortality.
Authors
- Y M Li (ORCID: https://orcid.org/0009-0001-5032-4789)
- Shanshan Zhang (ORCID: https://orcid.org/0000-0001-6458-8585)
- Yike Li (ORCID: https://orcid.org/0009-0006-1140-1194)
- M. Xiao (ORCID: https://orcid.org/0009-0006-6670-0757)
- Lin Lv
- Juan Zhang
- Yu-Hui Liu (ORCID: https://orcid.org/0009-0004-7700-3522)
Institutions
- Zhengzhou Jinshui District General Hospital (CN)
- Second Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-05-19
- DOI
- https://doi.org/10.2196/72349
- Citations
- 1
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 1.43