Calibration drift in regression and machine learning models for acute kidney injury

Key Points

• The aim is to outline the necessity of efficient model updating protocols to maintain prediction accuracy in acute kidney injury.
• Identified variations in calibration drift across different methods.
• Proposed protocols for updating models during rapid performance changes.
• Emphasized the importance of tailored updating frequencies rather than fixed intervals.
• Highlight that regular intervals for model updates are insufficient.
• Show that models need updates during performance drift for accurate predictions.
• Emphasize the need for personalized and responsive updating strategies.