
Predicting Kidney Injury After Cardiac Surgery With Cardiopulmonary Bypass Using Machine Learning
This study evaluates a machine learning (ML) model using electronic health record (EHR) data to predict acute kidney injury (AKI) after cardiac surgery. In 130 patients, the AI achieved strong predictive performance (AUROC 0.79 for AKI, 0.83 for 30-day kidney disease). The model enables early, automated risk stratification, offering a practical tool for proactive perioperative management and improved patient outcomes.




