JISE

A disease in traditional Chinese medicine is defined as a sequence of syndromes. The diagnosis of syndromes in traditional Chinese medicine is called syndrome differentiation. The construction of a syndrome differentiation system directly from clinical medical records using machine learning is still infeasible due to the lack of standardization of symptoms and syndromes in current clinical medical records. This article proposes a sophisticated approach to developing a syndrome differentiation system for 18 deficiency syndromes according to the knowledge of textbooks. This approach defines the syndrome differentiation problem as a membership problem of fuzzy sets. This approach designs a number of membership functions for fuzzy sets of syndromes based on a symptom grouping scheme and a symptom weighing scheme. Symptoms are grouped according to syndrome location, cause, and mechanism in the symptom grouping scheme. The symptom weighing scheme assigns exponentially decreasing weights to symptoms in each symptom group. An experimental evaluation based on a benchmark of 50 case reports shows that the proposed membership functions are very practical based on three differentiation metrics. This syndrome differentiation system can produce clinical medical records with standard symptoms and syndromes. In the future, these standard clinical medical records can be utilized to construct syndrome differentiation systems using machine learning.