A Novel Approach to Modelling a Diagnosis and Treatment of Traditional Vietnamese Medicine

Abstract

Traditional Vietnamese Medicine (TVM) is based on the experiences of thousands of years of Vietnamese people in the struggle against diseases; therefore, TVM is very important in the medical system of Vietnam. In this paper, we propose a novel model of an expert system for diagnosing disease syndromes and treating traditional Vietnamese medicine. In this model, the knowledge base consists of IF-THEN rules, in which the antecedent of a rule is an elementary conjunction of propositions and negated propositions. The inference mechanism for the diagnosis of disease syndromes and treatment of traditional Vietnamese medicine applies Abelian group operations. A comparison of the inference of our model with the fuzzy max-min inferences shows that our model can have very similar rules whose contributions sum up to high weight. On the other hand, in our model, a rule with a negative weight may diminish an effect of a rule with a good weight. This feature is absent in the systems with fuzzy max-min inferences. We have built rule patterns for the diagnosis of about 50 disease syndromes and their treatment by Herbs and Acupuncture with the cooperation of practitioners of Oriental Traditional Medicine in Vietnam. Some examples of databases and the rules for disease syndrome differentiation and treatment by herbal medicine and Acupuncture are shown. Finally, some conclusions and future works are given.