AGraphHF: Adversarial graph-based multimodal learning for heart failure risk prediction.

Abstract

Heart failure (HF) is a major cause of morbidity, mortality, and healthcare utilization worldwide, yet early risk stratification remains challenging because prognostic information is distributed across heterogeneous clinical narratives and subtle electrophysiological abnormalities. We propose AGraphHF, a multimodal framework for incident HF risk prediction that integrates 12-lead electrocardiograms (ECGs) with demographic-aware clinical text under a survival objective. The prediction task is temporally anchored at an index ECG and estimates the relative risk of subsequent HF onset under a longitudinal time-to-event formulation. The ECG encoder models individual leads as graph nodes and learns both inter-lead relationships and temporal dependencies using dual graph attention mechanisms. To enhance sensitivity to weak but prognostically relevant signal deviations, the model employs a two-phase adversarial training strategy with dual decoders, where stable reconstruction of typical ECG morphology is followed by deviation-focused refinement conditioned on reconstruction residuals. In parallel, clinical notes are encoded using a demographic-aware language model that extends BioClinicalBERT through age- and sex-conditioned contrastive pre-training. ECG and text representations are aligned in a shared latent space and fused hierarchically to produce patient-level risk estimates. We evaluate AGraphHF on high-risk cohorts derived from the MIMIC-IV database, including patients with hypertension and myocardial infarction. Experimental results demonstrate improved survival risk discrimination compared with established baselines on the hypertension and myocardial infarction cohorts, while also revealing cohort-dependent differences in the contribution of ECG and clinical text. These findings support the use of coordinated graph-based ECG modelling and demographic-aware multimodal integration for HF risk stratification, while emphasizing the need for cohort-specific interpretation of model performance.