EHR2HG: Modeling of EHRs Data Based on Hypergraphs for Disease Prediction

EHRs contain the patient’s historical disease information, and a natural idea is to predict the patient’s disease or diagnose the patient based on EHRs. However, existing deep learning models using EHRs are not satisfactory in solving several key challenges: 1) most of the existing methods are lack of priori knowledge assistance during model learning; and 2) higher-order relationships among diseases and patients are not explored sufficiently. To address these issues, in this paper we propose a hypergraph-based deep learning model for disease prediction, namely EHR2HG. First, we propose to utilize the existing disease classification information to give the model a better initialization condition. Second, by constructing hypergraphs, we consider all of the patients together instead of separate individuals, so our models can model entity relationships such as comorbidities or patient class groups. We have evaluated the proposed model on a real-world EHRs dataset and the results demonstrated that EHR2HG can achieve comparable and better performance than several state-of-the-art baseline methods.