Fig. 6: Summary of DynaCEL performance across models, datasets, and patient subpopulations.

This figure summarizes DynaCEL’s predictive performance across several modeling configurations and validation settings. a compares four base learners—random forest, MLP, SVM, and logistic regression—using a 12-hour predictor window and 24-hour outcome window, applied to the eICU test set cohort at MOP = 18 hr. b compares DynaCEL’s temporally stratified ensemble to a single pooled model trained across all MOPs with MOP included as a covariate; both used random forest and the same prediction window configuration as in panel a, evaluated on the same cohort. In c, external validation of DynaCEL is shown using the same random forest model applied to 18-hour MOP cohorts from the eICU test set, MIMIC-IV, and IUH datasets. d presents model performance across subpopulations defined by age, sex, race/ethnicity, BMI, comorbidity burden, and SOFA score using 18-hour MOP cohorts from the same three datasets. e shows HR–SBP–mortality contour maps from the MIMIC-IV cohort at MOP = 18 hr, illustrating mortality risk variation across HR–SBP combinations by subpopulation. DynaCEL Dynamic Cohort Ensemble Learning, 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, MIMIC-IV Medical Information Mart for Intensive Care, IUH Indiana University Health, BMI body mass index, CCI Charlson Comorbidity Index, SOFA Sequential Organ Failure Assessment, HR heart rate, SBP systolic blood pressure.