Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

TL;DR

This paper introduces a context-aware deep learning framework that models disease relationships and dynamics using transition functions on dynamic disease graphs, improving health event prediction accuracy from electronic health records.

Contribution

It proposes a novel dynamic disease graph model with transition functions and diagnosis roles to capture disease interactions and evolution over time.

Findings

Outperforms existing models in health event prediction accuracy.

Effectively captures disease co-occurrence and transition patterns.

Demonstrates robustness on real-world EHR datasets.

Abstract

With the wide application of electronic health records (EHR) in healthcare facilities, health event prediction with deep learning has gained more and more attention. A common feature of EHR data used for deep-learning-based predictions is historical diagnoses. Existing work mainly regards a diagnosis as an independent disease and does not consider clinical relations among diseases in a visit. Many machine learning approaches assume disease representations are static in different visits of a patient. However, in real practice, multiple diseases that are frequently diagnosed at the same time reflect hidden patterns that are conducive to prognosis. Moreover, the development of a disease is not static since some diseases can emerge or disappear and show various symptoms in different visits of a patient. To effectively utilize this combinational disease information and explore the dynamics of diseases, we propose a novel context-aware learning framework using transition functions on dynamic disease graphs. Specifically, we construct a global disease co-occurrence graph with multiple node properties for disease combinations. We design dynamic subgraphs for each patient's visit to leverage global and local contexts. We further define three diagnosis roles in each visit based on the variation of node properties to model disease transition processes. Experimental results on two real-world EHR datasets show that the proposed model outperforms state of the art in predicting health events.