MG-HetGNN: Metapath-Guided Heterogeneous Graph Neural Network with Semantic Aggregation for Medical Diagnosis

Deep learning-based medical diagnosis has become an essential research area with significant practical value and wide application prospects. However, modeling heterogeneous medical data, which contains diverse data types with different semantics, remains a challenge. Existing approaches mostly focus on single-modal data or fuse the data of each modality without considering the relationships between them. However, better results could be achieved when modeling heterogeneous data while considering the relationships between heterogeneous data. To address this issue, we propose a medical diagnosis method that formulates medical diagnosis as a link prediction task in heterogeneous graphs and develops a heterogeneous graph neural network MG-HetGNN. MG-HetGNN, which includes a metapath-based ego graph encoder and semantic-level feature fusion module, is a metapath- and attention-based heterogeneous graph neural network. Specifically, MG-HetGNN encodes the structural relationships under each semantics and aggregates semantic-level information to generate node embeddings. Experiments on public open datasets demonstrate that the MG-HetGNN has remarkable performance compared to the baselines.