Heterogeneous Semantic Graph Construction and HinSAGE Learning from Electronic Medical Records (Preprint)

BACKGROUND Graph representations learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records (EMR) datasets. Adapting the integration limits will support and advance the previous methods to predict the prognosis of patients in network models. OBJECTIVE This study aimed to address the challenge of implementing complex and large EMR, including the following: (1) demonstrating how to build a multi-attributed and multi-relational graph model (2) and applying a downstream disease prediction task of patient’s prognosis using HinSAGE algorithm. METHODS A total of 61,789 patients diagnosed with angina who visited the Asan Medical Center between 2000 and 2016 were included in the study. We illustrated two bipartite graph representations from real-world EMR data sets, to effectively visualize the patients’ representation and apply the heterogeneous graph neural network using the HinSAGE algorithm. Then, we performed the link prediction task for predicting the event occurrence of myocardial infarction, stroke, heart failure and death on binary edge attributes to evaluate the network performance. RESULTS The first constructed graph model contained 492,886 nodes with attributes and 439,045 edges for displaying 10 nodes and 9 edge types. The patient-centric graph database was then used to illustrate a query of a predictive network. Moreover, the second graph model is composed of 107,682 nodes and 53,841 edges with node and edge attributes for displaying 2 node and 2 edge types. Next, the model object underwent an experiment using the HinSAGE algorithm. As a result, the performance evaluation indicated that our heterogeneous graph model outperformed other baseline methods, achieving the area under a receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPRC) measurements of 0.69 and 0.17, respectively. CONCLUSIONS The proposed implemented models successfully demonstrated the graph construction and graph representation learning on the EMR dataset to empower three roles: broaden EMR research, decision making in diagnosis, and personalized medicine. Future works may integrate additional data sources alongside the node and edge types to further improve the performance evaluation.