Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction

TL;DR

This paper introduces a self-supervised hyperbolic graph learning framework that leverages hierarchical medical code representations to improve temporal health event prediction and interpretability from electronic health records.

Contribution

It proposes a novel hierarchy-aware hyperbolic embedding and a multi-level attention mechanism integrated into a self-supervised graph neural network for healthcare prediction tasks.

Findings

Enhanced prediction accuracy on EHR datasets

Improved interpretability of disease contributions

Effective utilization of hierarchical medical data

Abstract

Electronic Health Records (EHR) have been heavily used in modern healthcare systems for recording patients' admission information to hospitals. Many data-driven approaches employ temporal features in EHR for predicting specific diseases, readmission times, or diagnoses of patients. However, most existing predictive models cannot fully utilize EHR data, due to an inherent lack of labels in supervised training for some temporal events. Moreover, it is hard for existing works to simultaneously provide generic and personalized interpretability. To address these challenges, we first propose a hyperbolic embedding method with information flow to pre-train medical code representations in a hierarchical structure. We incorporate these pre-trained representations into a graph neural network to detect disease complications, and design a multi-level attention method to compute the contributions of particular diseases and admissions, thus enhancing personalized interpretability. We present a new hierarchy-enhanced historical prediction proxy task in our self-supervised learning framework to fully utilize EHR data and exploit medical domain knowledge. We conduct a comprehensive set of experiments and case studies on widely used publicly available EHR datasets to verify the effectiveness of our model. The results demonstrate our model's strengths in both predictive tasks and interpretable abilities.