Abstract: Automatically extracting useful information from electronic medical records (EMRs) and assisting in disease diagnosis has important theoretical and practical significance for clinical decision support and smart hospital construction. However, there is an imbalanced distribution of symptom data in EMRs, which leads to insufficient data volume for some diseases in assisted diagnosis. Moreover, traditional methods ignore the heterogeneity and multi-source contextual information of medical records, which can lead to poor disease prediction accuracy. This paper proposes a clinical assisted diagnosis prediction model HCAD based on heterogeneous graph medical record attention network. Firstly, the problem of imbalanced electronic medical record data is solved by constructing an external medical knowledge graph. Secondly, by effectively integrating patient condition descriptions and physiological records and designing node-level attention mechanisms and semantic relationship-level attention mechanisms, the importance of node and different semantic relationship information is identified. Finally, highly representative patient node vector representations are obtained through hierarchical aggregation, which accurately predicts diseases. Experiments on a real EMR dataset show that the proposed model has high feasibility, effectiveness, and interpretability, with an average F1 value improvement of 7.45% compared to the baseline.

Key words: electronic medical record, heterogeneous graph medical record network, meta-path, disease prediction