Diagnosis Ranking with Knowledge Graph Convolutional Networks

The automatic diagnosis of a medical condition provided the symptoms exhibited by a patient is at the basis of systems for clinical decision support, as well as for applications such as symptom checkers. Existing methods have not fully exploited medical knowledge: this likely hinders their effectiveness. In this work, we propose a knowledge-aware diagnosis ranking framework based on medical knowledge graph (KG) and graph convolutional neural network (GCN). The medical KG is used to model hierarchy and causality relationships between diseases and symptoms. We have evaluated our proposed method using realistic patient cases. The empirical results show that our knowledge-aware diagnosis ranking framework can improve the effectiveness of medical diagnosis.