Research on TCM syndrome differentiation based on multi-feature fusion and GCN

Abstract

Syndrome differentiation (SD) is a basic task in TCM (Traditional Chinese Medicine) diagnosis and treatment. TCM syndrome differentiation is very complex and time-consuming. Meanwhile, the accuracy of the results depends on the experience of TCM practitioners. To help TCM practitioners differentiate syndrome more quickly, we propose a syndrome differentiation method of deep learning based on multi-feature fusion. We extracted char, word and POS (Part of Speech) from TCM diagnosis and treatment records. The vector representation of char feature is obtained by ZY-BERT (Zhong Yi BERT), ZY-BERT was pre-trained on large datasets of TCM-SD (TCM Syndrome Differentiation). The vector representation of word and POS is obtained by Word2vec (Word to vector). We construct text graphs of char, word and POS according to context. GCN (Graph Convolutional Networks) is used to extract spatial structure information between multiple features to achieve multi-feature fusion. The experiment was carried out on TCM-SD. The experimental results showed that the accuracy of the proposed method was 81.52%, which was better than the comparison method. This method is helpful in the development of TCM modernization.