Fig. 1

Model performance by ROC AUC score for predicting improvement in various outcome parameters after turning patients to a prone position. The ROC AUC compares the true positive rate to the false positive rate where a performance of 1.0 reflects perfect scores where 0.5 describes complete randomness. LR logistic regression, RF  random forest, KNN  K-Nearest Neighbors, SVM  support vector machine, GNB Gaussian Naïve Bayes, XGB  eXtreme Gradient Boosting