TriGCN: Graph Convolution Network Based on Tripartite Graph for Personalized Medicine Recommendation System

Abstract

Intelligent medical systems have great potential to play an important role in people’s daily lives, as they can provide disease and medicine information immediately for both doctors and patients. Graph-structured data are attracting more and more attention in the artificial intelligence sector. Combining graph-structured data with a medical data set, a tripartite graph convolutional network named TriGCN is proposed. This model is able connect to disease and medicine or patient, disease, and medicine nodes, propagate information from layer to layer, and update node features at the same time. After this, calibrated label ranking is used to give personalized medicine recommendation lists to patients. The TriGCN approach has a great performance, outperforming other machine learning methods. Thus, this model has the potential to be applied in reality and will provide contributions to public health in the future.

Keywords

• tripartite graph
• graph convolutional network
• recommender system
• medicine recommendation