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

## TL;DR

This paper introduces a Structure-aware HyperGraph Transformer that captures complex higher-order relationships in electronic health records, significantly improving diagnosis prediction accuracy over existing models.

## Contribution

It proposes a novel hypergraph transformer framework that models higher-order medical code interactions and preserves hypergraph structure, advancing EHR-based predictive modeling.

## Key findings

- Outperforms state-of-the-art models on real-world EHR datasets
- Effectively captures higher-order dependencies among medical codes
- Improves diagnosis prediction accuracy

## 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.