Zhu Wen-Feng, Yan Jun-Feng, Huang Bi-Qun
Institute of Traditional Chinese Medicine Diagnosis, Hunan University of Traditional Chinese Medicine, Changsha, Hunan Province 410007, China.
Zhong Xi Yi Jie He Xue Bao. 2006 Nov;4(6):567-71. doi: 10.3736/jcim20060604.
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.
中医证候概念是一个非线性、开放且复杂的巨系统。中医辨证属于认知与思维科学。基于证候关键要素建立新的辨证体系，对于中医从业者提升辨证能力、在辨证方法上达成共识很有必要。结合经验与计算模型，贝叶斯网络被用于研究证候关键要素与症状之间的关系，以及不同关键要素之间的关系，其计算诊断结果与经验丰富的中医医生的诊断结果一致。研究表明，贝叶斯网络是处理症状和体征信息进行辨证的好方法，但它也不能全面反映中医医生辨证的思维能力。
