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The Research On Quantitative Diagnosis Of BSS And Its Simultaneous Type Of DM And The Principle Of Herbs Used By TCM Treatment

Posted on:2007-02-12Degree:MasterType:Thesis
Country:ChinaCandidate:W J YuFull Text:PDF
GTID:2144360212972878Subject:Traditional Chinese Medicine
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Object: To establish quantitative diagnosis blood stasis syndrome(BSS) and its simultaneous type of Type 2 Diabetes mellitus(DM) and its syndromes and study the principle of herbs used by TCM treatment. Method:1.The dissertation used literature investigation. We collected 795 cases from the periodicals in 12 years of DM (diabetes mellitus) diagnosis and treatment on the following two aspects:tradition Chinese medicine,chinese and western unite.2. Pretreatment data:we normally described syndromes, symptoms, physic -al signs, and herbs.3. Computer processed data and established database.4. The methods of data processing included frequency analysis, logistic regression analysis, maximum likelihood and analysis of the association rule.· Age and sex of patient was analyzed by frequency analysis.· We used frequency to analyze syndrome types and probed into Frequently BSS' s simultaneous type of type2-DM and its syndromes.· Symptoms and physical signs were analyzed as follow: firstly, symptoms and physical signs were analyzed by frequency analysis;secondly, logistic regression analysis model was used for analysis of symptoms and physical signs of every syndrome type; finally we used the method of maximum likelihood and established the diagnostic score table of every syndrome type and confirmed the critical diagnostic score.· Frequency analysis also was used to analyze herb .we analyzed virtue,distributing, nature and taste, meridian distribution of herbs; then we used analysis of the association rule to analyze herb combination, and discuss herb rule of compatibility of medicine.
Keywords/Search Tags:Diabetes mellitus (DM), blood stasis syndrome (BSS), quantitative diagnosis, the principle of herbs used by TCM treatment, Logistic Regression Analysis, maximum likelihood, Data Mining
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