Fig. 7: Model performance across base models and prediction configurations. | npj Digital Medicine

Fig. 7: Model performance across base models and prediction configurations.

From: Personalized and real time hemodynamic management in critical care using Dynamic Cohort Ensemble Learning (DynaCEL)

Fig. 7

This figure evaluates the effect of base model selection and prediction configuration on DynaCEL performance. The base model was treated as a hyperparameter, and AUC values were calculated across combinations of PW and OW using cohorts defined by MOPs in the eICU test set. Performance is shown for random forest, MLP, SVM, and logistic regression. Random forest consistently achieved the highest AUC across most configurations and was selected as the base model for subsequent analyses. AUC area under the receiver operating characteristic curve, MOP moment of prediction, PW predictor window, OW outcome window, MLP multilayer perceptron, SVM support vector machine, eICU eICU Collaborative Research Database.

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