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.

Methods

Using MIMIC-IV v3.1, we retained one first ICU stay per adult patient, required adequate pre-landmark creatinine observation, excluded coded end-stage kidney disease, documented dialysis before the landmark, and observed AKI by the landmark. The outcome was a new creatinine rise within the next 48 h using KDIGO-aligned creatinine change thresholds, with follow-up truncated at ICU exit, hospital discharge, or death. The ascertainable cohort (n = 15,231) was split 60%/15%/10%/15% into training, calibration, validation, and test sets. Logistic regression, random forests, and histogram gradient boosting were calibrated with a separate calibration set; thresholds were fixed on validation data to target at least 80% sensitivity. The test set was evaluated once. A later post hoc audit reused frozen test risks without retraining.