Development and validation of an interpretable machine learning model for predicting enteral nutrition-associated diarrhea in ICU patients: a multicenter study
The externally validated random forest model showed acceptable discriminative performance and provided an interpretable assessment of the contributions of individual features, which may support static early risk stratification of ENAD among critically ill patients receiving enteral nutrition.