aceToward 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…
The model's outcome ascertainment depended on observation, so many eligible patients had indeterminate outcomes and calibration and threshold performance shifted under stricter observation rules.
The study provides no external validation, patient-level explanation validation, or evidence of clinical benefit, and its outcome ascertainment depended on observation, leaving many patients with indeterminate outcomes.
Evidence
- Peer-reviewedInformation2026-10-20
How should this claim be treated?
Truvace Impact Record TRV-2026-2093, v1: “Toward Explainable AI for Acute Kidney Injury Prediction in the ICU: Transparent Development, Internal Validation, and Robustness Auditing of Machine Learning Models.” Truvace, 2026-10-21. /record/TRV-2026-2093 (accessed at citation time). sha256 ef756d84fc1bf148…
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