  Mining Electronic Health Records (EHRs) becomes a promising topic because of
the rich information they contain. By learning from EHRs, machine learning
models can be built to help human experts to make medical decisions and thus
improve healthcare quality. Recently, many models based on sequential or graph
models are proposed to achieve this goal. EHRs contain multiple entities and
relations and can be viewed as a heterogeneous graph. However, previous studies
ignore the heterogeneity in EHRs. On the other hand, current heterogeneous
graph neural networks cannot be simply used on an EHR graph because of the
existence of hub nodes in it. To address this issue, we propose Heterogeneous
Similarity Graph Neural Network (HSGNN) analyze EHRs with a novel heterogeneous
GNN. Our framework consists of two parts: one is a preprocessing method and the
other is an end-to-end GNN. The preprocessing method normalizes edges and
splits the EHR graph into multiple homogeneous graphs while each homogeneous
graph contains partial information of the original EHR graph. The GNN takes all
homogeneous graphs as input and fuses all of them into one graph to make a
prediction. Experimental results show that HSGNN outperforms other baselines in
the diagnosis prediction task.
