How to recommend an effective prescription with intelligent assistant treatment remains a key issue in Traditional Chinese Medicine (TCM). To address this issue, various intelligent assistant treatment methods have been developed in recent years. However, existing methods barely integrate together knowledge graphs of TCM and the characteristics of individual differences between patients, which play very important roles in the prescription recommendation of TCM. For this reason, this work proposes a novel framework of TCM prescription recommendation, referred to as CETCMKG , which integrates the TCM knowledge graph to improve the accuracy and interpretability of prescription recommendation. More specifically, features of herb and symptom nodes are learned by combining the graph embedding models and graph convolutional neural networks. During the prediction and recommendation phase, multi-head attention mechanisms and multi-layer perceptrons jointly analyze symptom patterns to guide herb selection. Following this analysis, the performance of CETCMKG is rigorously evaluated through comparative experiments, demonstrating its superiority over existing state-of-the-art prescription recommendation methods in TCM.
Zhang et al. (Sat,) studied this question.