DualStream-MTCA: A Hybrid Deep Learning Model for the Simultaneous Early Detection of Sepsis and Heart Failure in Adult Intensive Care

Sepsis and heart failure share early-warning physiology but require divergent treatments, complicating early intensive care intervention. Current predictive models address these conditions independently. We present DualStream-MTCA, a hybrid deep-learning architecture for the simultaneous early detection of both conditions. Trained on 53,229 ICU stays from MIMIC-IV v3.1, the model combines dual Bidirectional LSTM streams, encoding vital signs and laboratory results, with multi-head cross-attention and XGBoost leaf embeddings. On a held-out test set, the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.867 for sepsis and 0.899 for heart failure. It demonstrated excellent probability calibration (expected calibration error < 0.015) and positive net clinical benefit. External validation on 102,695 eICU-CRD stays showed strong generalizability for heart failure (AUROC drop 0.036), while sepsis performance drops were traced to external data sparsity.