摘要：
Differentiating syndrome factor and forming syndrome type according to symptoms and signs are the rules and processes of syndrome differentiation in traditional Chinese medicine (TCM). TCM syndrome differentiation is a nonlinear complex giant system. In order to solve the key problem of determination of diagnosing weight value for syndrome factor differentiation, a new algorithm of double levels of frequency and weight based on the analysis of frequency statistics was applied, and the accurate syndrome differentiation parameters were acquired. Therefore, based on the nonlinear and multivariate analysis, a new algorithm of calculating diagnostics for syndrome factor differentiation was established.
通讯机构：
Institute of Traditional Chinese Medicine Diagnosis, Hunan University of Traditional Chinese Medicine, China
关键词：
Bayesian network;Key pattern elements;Syndrome differentiating system;Traditional Chinese medicine
摘要：
The concept of syndrome in traditional Chinese medicine (TCM) is a nonlinear, open and complicated huge system. Syndrome differentiation in TCM belongs to cognitive and noetic science. To establish a new syndrome differentiation system based on the key elements of the syndrome is necessary for TCM practitioners to promote differentiation ability and reach consensus on differentiation method. With combination of experience and computation models, the Bayesian network was used in the study of the relationship between the key elements of syndrome and the symptoms, and the relationship among different key elements, in which the computing diagnosis result was identical to the result from an experienced TCM doctor. The study showed that Bayesian network is a good method to deal with the information of symptoms and signs for syndrome differentiation, but it is also not to reflect comprehensively the thinking ability of TCM doctors in doing syndrome differentiation.
