  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.
