My research project has been supported by National Basic Research Program of China (973), “Basic research on standards of the syndrome defined by Chinese medicine and its correlation with diseases and formulas”. The main task is to analyze the complicated correlations among syndromes, diseases, and formulas of Traditional Chinese Medicine, to evaluate the symptom indices synthetically, to diagnose the syndromes according to collected four diagnoses information, and to try to discover the evolution rules of the complex system composed of syndromes, diseases, and formulas, by using the entropy method of complex system partition, cross entropy minimization based intelligent system principles, and entropy based intelligent calculation methods. According to the project requirement, I have finished following work. 1 The study of correlation of complex system In this thesis, mutual information method based on information entropy is studied to analyze the correlations among the indices of TCM system. Mutual information methods defined on Shannon and Renyi’s entropy are studied and a method that combines parameter estimation with mutual information is proposed to analyze the correlation between syndromes of TCM and physicochemical parameters. 2 The study of synthetic evaluation of multi-index complex system In this thesis, a generalized index building method based on entropy is studied and it is applied to the synthetic evaluation of TCM indices. 3 Objectively differentiation of syndromes combines mutual information based feature selection and Support Vector Machines In this paper, feature selection based on mutual information is studied to select the most informative symptom combination and they are taken as the standard for objectively differentiation of syndromes. A fast algorithm for calculating the combination mutual information of discrete variables is proposed. A novel definition of contribution rate based on mutual information is proposed and it is used as the stopping rule of feature selection. The selected symptoms combination is input to SVM classifier for objectively differentiation of syndromes. 4 The study of unsupervised clustering methods Two unsupervised clustering methods are studied in this thesis, i.e. high order unsupervised Boltzmann machine and unsupervised clustering method based on extended entropy. They are used to analyze the clustering of clinic cases and symptoms respectively. 5 System model of disease, syndrome, and formula of TCM In this thesis, we take use of intelligent system to simulate the process of differentiation of syndromes. Through analyzing the intelligent system, we propose an intelligent differentiation of syndromes model that is realized with determined Boltzmann neural network. At last, summarize the research result and prospect the future research work.
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