Fig. 2
From: Developing and validating machine learning models to predict next-day extubation
(A) Performance metrics of different ML models. The receiver operating characteristic curve (ROC), precision-recall curve (PRC) plots of different ML models on same test set along with values of respective area under the curves, using each model’s best-performing imputation method, including extreme gradient boosting (XGBoost), Recurrent Neural Network (RNN), and long short-term memory (LSTM), on the test set, using different imputation strategies (raw and binning, detailed in Methods). Curves displayed are for a single pass through the test set. Full metrics and confidence intervals for the top optimized models shown are in Supplemental Table 4. (B) Model performance on external test cohort. We applied our top-performing binned LSTM model (based on AUROC) to a patient cohort from a different hospital system as an external test. ROC and curves show similar performance to the SCRIPT test set in Fig. 2. Curves displayed are for a single pass through the test set. Full metrics and confidence intervals for the top optimized models shown are in Supplemental Table 4.
