TCM herbal prescription recommendation model based on multi-graph convolutional network.

ETHNOPHARMACOLOGICAL RELEVANCE: The recommendation of herbal prescriptions is a focus of research in traditional Chinese medicine (TCM). Artificial intelligence (AI) algorithms can generate prescriptions by analysing symptom data. Current models mainly focus on the binary relationships between a group of symptoms and a group of TCM herbs. A smaller number of existing models focus on the ternary relationships between TCM symptoms, syndrome-types and herbs. However, the process of TCM diagnosis (symptom analysis) and treatment (prescription) is, in essence, a "multi-ary" (n-ary) relationship. Present models fall short of considering the n-ary relationships between symptoms, state-elements, syndrome-types and herbs. Therefore, there is room for improvement in TCM herbal prescription recommendation models. PURPOSE: To portray the n-ary relationship, this study proposes a prescription recommendation model based on a multigraph convolutional network (MGCN). It introduces two essential components of the TCM diagnosis process: state-elements and syndrome-types. METHODS: The MGCN consists of two modules: a TCM feature-aggregation module and a herbal medicine prediction module. The TCM feature-aggregation module simulates the n-ary relationships between symptoms and prescriptions by constructing a symptom-'state element'-symptom graph (Se) and a symptom-'syndrome-type'-symptom graph (Ts). The herbal medicine prediction module inputs state-elements, syndrome-types and symptom data and uses a multilayer perceptron (MLP) to predict a corresponding herbal prescription. To verify the effectiveness of the proposed model, numerous quantitative and qualitative experiments were conducted on the Treatise on Febrile Diseases dataset. RESULTS: In the experiments, the MGCN outperformed three other algorithms used for comparison. In addition, the experimental data shows that, of these three algorithms, the SVM performed best. The MGCN was 4.51%, 6.45% and 5.31% higher in Precision@5, Recall@5 and F1-score@5, respectively, than the SVM. We set the K-value to 5 and conducted two qualitative experiments. In the first case, all five herbs in the label were correctly predicted by the MGCN. In the second case, four of the five herbs were correctly predicted. CONCLUSIONS: Compared with existing AI algorithms, the MGCN significantly improved the accuracy of TCM herbal prescription recommendations. In addition, the MGCN provides a more accurate TCM prescription herbal recommendation scheme, giving it great practical application value.

[Research and analysis of data source--analysis of the items of medical plants in Xiandai Bencao Gangmu].

Scientific data is the source of innovation in knowledge. In order to change the situation that there is few information in plenty of data and to obtain useful knowledge which has high information content, it is necessary to clean data and ensure data's accuracy and without noise off when database is established initially. High-quality data comes from high-quality data source. But incomplete and incorrect and irregular data exist widely in the data source of Chinese materia medica. The phenomenon of synonyms and homonym is quite serious, and there is no unified description for the name and origin of Chinese materia medica among different data sources. So data processing including data analysis and research is very important in the establishment of Chinese materia medica database. In order to get the most accurate and standard data, this paper analyzed the items of Medical Plants in Xiandai Bencao Gangmu, including classification analysis of medical plants: distribution analysis of different classes and analysis of medical part; analysis of synonyms and homonym; analysis of incorrect data and analysis of advantage and disadvantage of data sources.