Word-computing algorithm based on data envelopment analysis with the corresponding dose effect-medicinal patterns of Shanghanlun / 中国药学杂志

OBJECTIVE: To construct the data envelopment analysis (DEA) inference model for word-computing. METHODS: Firstly, a syndrome assembly was established based on the Chinese medical patterns contained in Shanghanlun, then the subjective evaluation was set by the linguistic description for the corresponding patterns. Secondly, with the data of decoctions, the evaluation was modified according to the requirement of DEA to construct the inference model. Finally, the word-computing was completed by the inference function of this model. RESULTS: DEA model could describe the diagnostic thinking process of Shanghanlun theory. CONCLUSION: DEA model can upload the information embodied among the words of the corresponding classical doses and symptoms, and it provides a kind of method for modernization of traditional Chinese medicine.

Study on syndrome quantification, differentiation and classification of traditional Chinese medicine with data envelopment analysis / 中国中药杂志

To raise the syndrome sequence quantification, differentiation and classification algorithm based on data envelopment analysis for solving the modeling issue of syndrome differentiation and classification of traditional Chinese medicine (TCM). This algorithm has three steps: first, in order to obtain basic units for explaining pathogenesis, and establish a syndrome collection on this basis mechanisms of syndrome differentiation and classification were analyzed and classified according to TCM theory, mechanisms of syndrome differentiation and classification were analyzed and classified according to TCM theory; second, regularity and syndromes of corresponding prescriptions were sought according to the incidence and development progress of syndromes, and mathematical tools of data envelopment analysis were used to calculate state data of syndromes in each stage and obtain quantitative syndrome sequence; finally, syndrome sequence was taken as the measurement standard to quantify candidate syndromes and diagnostic information, and the similarity was calculated to obtain the matching degree between diagnostic information and candidate syndromes, so as to complete the syndrome differentiation and classification calculation. According to the results of model-based reasoning, the algorithm could indicate the regularity implied in prescription materials, and grasp the dynamic process of syndromes in an all-round way, and its results were verified through calculation and analysis on clinical cases. At least, it provides an idea for quantitative modeling of TCM.