Graph-based medicine embedding learning via multiple attentions

Clinical knowledge reasoning provides a new perspective for intelligent diagnosis and therapy. This study constructs a multi-relational medicine-attribute network, which refers to three different types of relationships: inter-medicine, inter-attribute, and medicine-attribute. Then, a multi-relation-based graph attention network (GAT) model combined with the spectral clustering (SC) algorithm, termed MGAT-SC, is presented to train the medicine embeddings and explore the regularity of medicine combinations. Based on real-world clinical data, the GAT model realizes the information aggregation of neighbor nodes and generates the embeddings of medicines and their attributes via a multi-head attention mechanism. Then, the SC method is utilized to divide the medicines and their embeddings. We design and conduct a series of experiments to verify the model’s performance. The experimental results show that the MGAT-SC outperforms the representative baselines and their variants in terms of five evaluation metrics and achieves around 6.0% improvement in accuracy compared to the SC algorithm.