A Configurable Multi-Modal Clinical Time-Series Platform for Predictive Modeling in Cardiology

Background/objective

Predictive models for cardiovascular disease rely predominantly on electronic health records (EHRs), whereas wearable devices, home monitoring, and patient-reported outcomes offer new opportunities for multimodal risk prediction; however, integrating these heterogeneous sources into a reproducible, clinically usable machine learning workflow remains challenging. This study aimed to develop an end-to-end architecture for explainable multimodal time-series prediction in patients with heart failure (HF), left ventricular assist device (LVAD) recipients, and heart transplant (HT) recipients.