Heterogeneous Graph Construction and HinSAGE Learning from Electronic Medical Records

Overview

Journal Sci Rep

Specialty Science

Date 2022 Dec 8

PMID 36477457

Authors

Ha Na Cho

Imjin Ahn

Hansle Gwon

Hee Jun Kang

Yunha Kim

Hyeram Seo

Heejung Choi

Minkyoung Kim

Jiye Han

Gaeun Kee

Tae Joon Jun

Young-Hak Kim

Affiliations

Soon will be listed here.

Abstract

Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to address the challenge of implementing a complex and highly heterogeneous dataset, 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 a patient's prognosis using the HinSAGE algorithm. We present a bipartite graph schema and a graph database construction in detail. The first constructed graph database illustrates a query of a predictive network that provides analytical insights using a graph representation of a patient's journey. Moreover, we demonstrate an alternative bipartite model where we apply the model to the HinSAGE to perform the link prediction task for predicting the event occurrence. Consequently, the performance evaluation indicated that our heterogeneous graph model was successfully predicted as a baseline model. Overall, our graph database successfully demonstrated efficient real-time query performance and showed HinSAGE implementation to predict cardiovascular disease event outcomes on supervised link prediction learning.

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