Assessing Calibration And Bias Of A Deployed Machine Learning Malnutrition Prediction Model Within A Large Healthcare System

This article discusses the importance of calibration in predictive models, specifically in the context of electronic health records (EHR). The authors assess the calibration of an actively deployed malnutrition predictive model and its performance in identifying high-risk patients. The study highlights the need for continued evaluation and recalibration of predictive models in healthcare.