Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction

TL;DR

This paper presents a novel time-aware heterogeneous graph transformer with adaptive attention for health event prediction, effectively integrating disease knowledge, temporal dynamics, and heterogeneous data to improve accuracy and interpretability in EHR-based models.

Contribution

The paper introduces a new heterogeneous graph model that incorporates temporal data and adaptive attention, enhancing disease representation and prediction in healthcare applications.

Findings

Improved prediction accuracy over existing methods

Enhanced interpretability of health event models

Effective integration of disease domain knowledge and temporal dynamics

Abstract

The widespread application of Electronic Health Records (EHR) data in the medical field has led to early successes in disease risk prediction using deep learning methods. These methods typically require extensive data for training due to their large parameter sets. However, existing works do not exploit the full potential of EHR data. A significant challenge arises from the infrequent occurrence of many medical codes within EHR data, limiting their clinical applicability. Current research often lacks in critical areas: 1) incorporating disease domain knowledge; 2) heterogeneously learning disease representations with rich meanings; 3) capturing the temporal dynamics of disease progression. To overcome these limitations, we introduce a novel heterogeneous graph learning model designed to assimilate disease domain knowledge and elucidate the intricate relationships between drugs and diseases. This model innovatively incorporates temporal data into visit-level embeddings and leverages a time-aware transformer alongside an adaptive attention mechanism to produce patient representations. When evaluated on two healthcare datasets, our approach demonstrated notable enhancements in both prediction accuracy and interpretability over existing methodologies, signifying a substantial advancement towards personalized and proactive healthcare management.