Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks

• URL: http://arxiv.org/abs/2009.02625v2
• Date: Sat, 13 Feb 2021 01:47:01 GMT
• Title: Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks
• Authors: Zifeng Wang and Rui Wen and Xi Chen and Shilei Cao and Shao-Lun Huang and Buyue Qian and Yefeng Zheng
• Abstract summary: We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs) We first organize the EHR data into a heterogeneous graph that is capable of modeling complex interactions among users, symptoms and diseases. Then, we build a disease self-diagnosis system with a corresponding EHR Graph-based Symptom Retrieval System (GraphRet) that can search and provide a list of relevant alternative symptoms.
• Score: 34.88155389791519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). Two main challenges are focused in this paper for online disease diagnosis: (1) serving cold-start users via graph convolutional networks and (2) handling scarce clinical description via a symptom retrieval system. To this end, we first organize the EHR data into a heterogeneous graph that is capable of modeling complex interactions among users, symptoms and diseases, and tailor the graph representation learning towards disease diagnosis with an inductive learning paradigm. Then, we build a disease self-diagnosis system with a corresponding EHR Graph-based Symptom Retrieval System (GraphRet) that can search and provide a list of relevant alternative symptoms by tracing the predefined meta-paths. GraphRet helps enrich the seed symptom set through the EHR graph when confronting users with scarce descriptions, hence yield better diagnosis accuracy. At last, we validate the superiority of our model on a large-scale EHR dataset.

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