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. Results: Histogram gradient boosting was selected by validation average precision. In 2286 test patients (664 events; 29.0%), AUROC was 0.726 (95% CI, 0.701–0.749), average precision 0.523 (0.478–0.565), and Brier score 0.179 (0.171–0.187). At the fixed threshold, sensitivity was 78.5%, specificity 52.2%, positive predictive value 40.2%, and false alerts 33.9 per 100 patients. Mean predicted risk was 0.290 versus an observed event fraction of 0.290; calibration intercept and slope were 0.023 and 1.022. The AUROC difference versus full logistic regression was +0.0197 (95% CI, 0.00235–0.03571). Frozen-model sensitivity analyses produced AUROCs from 0.697 to 0.733 in most observation and measurement subsets. However, 15,900 otherwise eligible patients had indeterminate outcomes and showed substantially shorter follow-up and fewer post-landmark creatinine measurements. Conclusions: The selected model showed moderate discrimination and close aggregate calibration under internal validation. Discrimination was similar across several post hoc subsets, although calibration and threshold-based performance changed under stricter observation rules. The main limitation was observation-dependent outcome ascertainment. This work supports transparent model auditing as part of trustworthy AI evaluation, but it does not provide external validation, patient-level explanation validation, or evidence of clinical benefit. Multicenter temporal validation and prospectively defined observation rules are needed before clinical use.