Construction of Knowledge Base Model of Syndromes and Prescriptions in Shanghan Lun Based on Artificial Neural Network / 世界科学技术-中医药现代化

ABSTRACT

This study was aimed to explore the nonlinear relationship between syndromes and prescriptions by using the artificial neural network (ANN) algorithm. The clauses composed of syndromes and prescriptions in Shanghan Lun (Treatise on Cold Damage and Miscellaneous Diseases) were sorted out. A total of 245 clauses were trained with ANN to establish the knowledge base model of syndromes and prescriptions. After that, 100 clauses were tested. And then, the symptoms of Gui-Zhi-Tang (GZT) syndrome, Ma-Huang-Tang (MHT) syndrome, Xiao-Chai-Hu-Tang (XCHT) syndrome and Bai-Hu-Tang (BHT) syndrome were input into the model to predict the Chinese herbs. The results showed that the test accuracy of the model was 79%. For the herbs to GZT and MHT syndrome, the test error was less than 0.1. For the main herbs to XCHT and BHT syndrome, the test error was less than 0.1. While for other herbs, the test error was less than 0.3. It was concluded that the ANN algorithm can simulate the nonlinear relationship between syndromes and prescriptions, which can be applied to study the syndrome differentiation, in order to contribute to the syndromes and prescriptions standardization and the traditional Chinese medicine (TCM) information.