Structure-aware Hypergraph Transformer for Diagnosis Prediction in Electronic Health Records

• URL: http://arxiv.org/abs/2508.20500v1
• Date: Thu, 28 Aug 2025 07:37:45 GMT
• Title: Structure-aware Hypergraph Transformer for Diagnosis Prediction in Electronic Health Records
• Authors: Haiyan Wang, Ye Yuan,
• Abstract summary: Graph neural networks (GNNs) have demonstrated effectiveness in modeling interactions between medical codes within EHR.<n>This paper proposes a novel Structure-aware HyperGraph Transformer (SHGT) framework following three-fold ideas.<n> Experiments on real-world EHR datasets demonstrate that the proposed SHGT outperforms existing state-of-the-art models on diagnosis prediction.
• Score: 7.359896812163171
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electronic Health Records (EHR) systematically organize patient health data through standardized medical codes, serving as a comprehensive and invaluable source for predictive modeling. Graph neural networks (GNNs) have demonstrated effectiveness in modeling interactions between medical codes within EHR. However, existing GNN-based methods are inadequate due to: a) their reliance on pairwise relations fails to capture the inherent higher-order dependencies in clinical data, and b) the localized message-passing scheme limits representation power. To address these issues, this paper proposes a novel Structure-aware HyperGraph Transformer (SHGT) framework following three-fold ideas: a) employing a hypergraph structural encoder to capture higher-order interactions among medical codes, b) integrating the Transformer architecture to reason over the entire hypergraph, and c) designing a tailored loss function incorporating hypergraph reconstruction to preserve the hypergraph's original structure. Experiments on real-world EHR datasets demonstrate that the proposed SHGT outperforms existing state-of-the-art models on diagnosis prediction.

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