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Professor Dai Enlai's Clinical Experience In The Diagnosis And Treatment Of Hematuria And Proteinuria

Posted on:2018-09-02Degree:MasterType:Thesis
Country:ChinaCandidate:Y ZhangFull Text:PDF
GTID:2334330536458654Subject:Traditional Chinese Medicine
Abstract/Summary:PDF Full Text Request
Objective:Summarized Professor Dai Enlai's clinical experience of treating hematuria and proteinuria,expanded clinical treatment,inherited academic thought?Methods:1.Wrote and arranged Professor Dai Enlai's theory and academic viewpoint on hematuria and proteinuria,elaborated Professor's understanding,treatment principle and medication,listed typical medical cases?2.Collected the outpatients' records from Professor Dai of Affiliated Hospital of Gansu University traditional Chinese Medicine,the TCM inheritance support system was used to input information and data,obtained the drug frequency,used association rules analysis to obtain the prescription regularity,used complex system entropy cluster and unsupervised hierarchical cluster to obtain new prescriptions,induced the prescription medication regularity of treating hematuri a and proteinuria by Professor Dai Enlai?Results:1.The academic thought of Professor Dai Enlai's treating hematuria is“nou rishing yin and stopping bleeding,dispelling toxin and resolving Stasis”,there are four therapeutic methods: dispelling wind and clearing heat,clearing heat and cooling blood to stop bleeding,promoting blood circulation to stop bleeding,nourishing Yin to stop bleeding?Through frequency statistics analysis,obtained Treatment of hematuria frequency of the top ten drugs by Professor Dai Enlai as follow: Pseudo-ginseng?Fructus Kochiae?Semen Cuscutae?Amber?Astrag alus membranaceus?Yam?Nightshade?Honeysuckle?Ovientvine?Through the analysis of association rules algorithm,resulting in a higher frequency of 10 pairs of drugs?Based on the improved mutual information method,7 drug pairs with high correlation were obtained?According to the complex system entropy clustering algorithm,10 core combinations are obtained?5 new prescriptions were obtained according to the unsupervised entropy hierarchical clustering algorithm?Draw a conclusion:the analysis of Professor Dai Enlai's common medicine for hematuria is consistent with the academic thought“nourishing yin and stopping bleeding,dispelling toxin and resolving Stasis”?2.The academic thought of Professor Dai Enlai's treating proteinuria is“tonifying the kidney and consolidating the essence,detoxicating and dredging coll aterals”,there are five therapeutic methods: reinforcing and fixing,promoting blood circulation,clearing damp heat,dispelling wind and removing obstruction in the meridians,warming and tonifying Kidney Yang?Through frequency statistics analysis,obtained Treatment of proteinuria frequency of the top ten drugs by Professor Dai Enlai as follow: Pberetima?Angelica?Leech?Semen Cuscutae?Yam?Astragalus membranaceus?fructus ligustri lucidi?Fructus Kochiae?Herba epimedii?Nightshade?Through the analysis of association rules algorithm,resulting in a higher frequency of 10 pairs of drugs?Based on the improved mutual information method,9 drug pairs with high correlation were obtained?According to the complex system entropy clustering algorithm,14 core combin ations are obtained?7 new prescriptions were obtained according to the unsupe rvised entropy hierarchical clustering algorithm?Draw a conclusion:the analysi s of Professor Dai Enlai's common medicine for proteinuria is consistent with the academic thought“tonifying the kidney and consolidating the essence,detoxicating and dredging collaterals”?Conclusion:1.Professor Dai Enlai has abundant clinical experience and his treatment ofhematuria and proteinuria has an affirmative curative effect?2.Through the TCM proved that there are certain regularities on Professor Dai Enlai 's clinical prescription medication?TCM can analysis clinical prescription medication regularity,form new prescription?It plays an important role in inheriting famous doctor of traditional Chinese medicine's experience?...
Keywords/Search Tags:Hematuria, proteinuria, clinical experience, data mining
PDF Full Text Request
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