HEAL-Net: Heterogeneous Evidence-Adaptive Learning for Multi-Task Clinical Prediction

Electronic health records contain heterogeneous clinical evidence, including diagnoses, procedures, medications, laboratory measurements, and longitudinal encounter histories. Integrating these sources is challenging because they differ in semantic structure, clinical relevance, and reliability, while downstream prediction tasks require different uses of current and historical information. We introduce **HEAL-Net**, a **H**eterogeneous **E**vidence-**A**daptive **L**earning **Net**work for clinical prediction. HEAL-Net models patient records as visit-aware heterogeneous graphs with typed concepts, severity-aware labs, and knowledge-based relations. Its relational attention encoder and learnable temporal kernel integrate heterogeneous evidence across visits. Task-specific heads support mortality/readmission prediction, medication recommendation, and length-of-stay estimation through tailored historical fusion and output adjustments. Experiments on MIMIC-III/IV across four tasks show that HEAL-Net achieves the best reported mortality and readmission AUPRC/AUROC, improves AUPRC over GraphCare by 2.40–4.06 points, and attains the highest length-of-stay AUROC and medication sample-F1. Ablations validate graph structure, temporal encoding, and laboratory representation.