CKD risk prediction models achieve AUROC 1.00 internally but drop to 0.48-0.58 externally with high calibration error and low deployment scores, indicating need for external validation.
Assessing calibration and bias of a deployed machine learning malnutrition prediction model within a large healthcare system
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Calibration, Uncertainty Communication, and Deployment Readiness in CKD Risk Prediction: A Framework Evaluation Study
CKD risk prediction models achieve AUROC 1.00 internally but drop to 0.48-0.58 externally with high calibration error and low deployment scores, indicating need for external validation.