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

Conclusions:</strong> Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making. Model updating protocols should be tailored to account for variations in calibration drift across methods and respond to periods of rapid performance drift rather than be limited to regularly scheduled annual or biannual intervals.</span>
Source: Journal of the American Medical Informatics Association - Category: Information Technology Source Type: research