Interpretable machine learning models for predicting venous thromboembolism in the intensive care unit: an analysis based on data from 207 centers.

ML models can be a reliable tool for predicting VTE in critically ill patients. Among all the models we had constructed, the random forest model was the most effective model that helps the user identify patients at high risk of VTE early so that early intervention can be implemented to reduce the burden of VTE on the patients.