Toward Explainable AI for Acute Kidney Injury Prediction in the ICU: Transparent Development, Internal Validation, and Robustness Auditing of Machine Learning Models

Background/Objectives: Acute kidney injury (AKI) is common in critical care, yet predictive artificial intelligence studies often differ in outcome definitions, validation, calibration, and interpretability. We developed and internally validated machine learning models for newly observed serum-creatinine-defined AKI after a fixed 24 h ICU landmark and then audited how stable the selected model was to clinically important observation and timing choices.