MultiCardioNet: A Multimodal Deep Learning Model for Early Cardiovascular Deterioration Prediction in ICU Patients

Cardiovascular deterioration is a clinically important event in intensive care units, but early prediction remains challenging because risk may be reflected across structured clinical variables, physiological time series, and clinical text. In this work, we present MultiCardioNet, a multimodal deep learning framework for early prediction of a composite ICU deterioration endpoint using MIMIC-IV data. The prediction target was defined as vasopressor initiation and/or early death during the 24–72 h outcome window, making the task broader than mortality prediction alone but also related to treatment escalation and circulatory support. The model combines structured clinical variables, 24-h vital sign time series, and timestamp-filtered radiology reports. Structured information was represented using enriched first-24-h clinical features, while physiological dynamics were modeled using a transformer-based time series encoder and radiology reports were represented using CXR-BERT-specialized embeddings. On the held-out test set, MultiCardioNet achieved an AUROC of 0.9014, AUPRC of 0.8481, and F1-score of 0.7723. These findings suggest that the three modalities provide complementary information for this composite deterioration endpoint in the internal test setting.