Patient-Visit-Spanned Hypergraph Learning for EHR-based Diagnosis Prediction

Abstract

Hypergraphs effectively model complex interactions in structured Electronic Health Records (EHR). Consequently, Hypergraph Neural Networks (HGNNs) are commonly applied to EHR-based diagnosis prediction. However, existing HGNNs struggle to capture patient-visit long-range dependencies when processing EHR-derived hypergraphs. To tackle this issue, we propose a Patient-Visit-Spanned HyperGraph Learning (PVHGL) framework specifically designed for diagnosis prediction. Concretely, PVHGL initially constructs a unified patient-visit hypergraph that integrates visit records from all patients, enabling the capture of shared healthcare patterns across the patient population. Subsequently, it incorporates a Transformer architecture enhanced by two structure-encoding matrices to facilitate one-step message propagation, which preserves both local and global hypergraph structural properties effectively. Additionally, the framework integrates a medical code co-occurrence matrix to explicitly guide the learning process by highlighting critical medical code interactions. Comprehensive experiments on three real-world datasets demonstrate that the proposed PVHGL significantly outperforms state-of-the-art baselines in diagnosis prediction.