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Research On Professor Liu Chenggang’s Experience In Treating Postinfectious Cough Based On Data Mining

Posted on:2023-07-04Degree:MasterType:Thesis
Country:ChinaCandidate:Y WuFull Text:PDF
GTID:2544306803489874Subject:Internal medicine of traditional Chinese medicine
Abstract/Summary:PDF Full Text Request
Objective: A variety of data analysis software was used for data mining and analysis of Professor Liu Chenggang’s medical records of patients with PIC,summed up Professor Liu Chenggang’s experience in treating PIC and medication rules,and provided new ideas for clinical treatment of the disease.Methods: From September 2019 to September 2021,a total of 142 standard medical records of PIC patients in the outpatient medical record system of Professor Liu Chenggang of the Second Hospital of Heilongjiang University of Traditional Chinese Medicine were screened.Enter the medical record data into Microsoft office Excel 2019 to establish a database and conduct standardized processing.The ancient and modern medical record cloud platform(V2.3.5),IBM SPSS 23.0,and SPSS Modeler 18.0 software were used for data analysis,and summed up Professor Liu Chenggang’s experience in treating PIC and medication rules.Result: Statistics show that there are 123 flavors of drugs in all 142 prescriptions,and the total frequency of drugs appears 1477 times.The results of the four qi of the drugs showed that the warm drugs were used the most,and the overall medicinal properties of cold and heat are not obvious.The statistical results of the five flavors showed that the pungent drugs were used the most,and the bitter and sweet drugs accounted for a large proportion.The statistical results of meridian return showed that the medicines of the lung meridian were used the most,and the medicines of the spleen,stomach and heart meridians accounted for a larger proportion.Efficacy statistics show that the most commonly used medicines are exterior-releasing medicines,with a large proportion of tonic medicines,phlegm-relieving and cough-suppressing medicines,and heat-clearing medicines.Cluster analysis obtained seven groups of commonly used drug combinations and eight pairs of commonly used drugs.The medical records involved a total of seven patterns,and the commonly used prescriptions for each patterns were obtained from statistics.A total of 51 kinds of concurrent symptoms were found,with a total frequency of 227 times.Association analysis yielded a table of associations between drugs and concurrent symptoms.Conclusion:1.Professor Liu Chenggang used warm medicines most often to treat PIC,but there was no obvious tendency to cold and heat;he commonly used medicinal flavors are pungent,bitter,and sweet;he commonly used drugs are classified into lung,spleen,stomach,and heart meridians;he commonly used drugs Efficacy are exterior-releasing medicines,tonic medicines,phlegm-relieving and cough-suppressing medicines,and heat-clearing medicines.2.Professor Liu Chenggang is good at treating PIC by different patterns.He has experienced prescriptions for each pattern and often adjusts them according to the changes of concurrent symptoms.These prescriptions can be summarized into a variety of drug pairs and drug combinations,which can provide new solutions for the clinical treatment of this disease.3.Professor Liu Chenggang attaches great importance to the treatment of PIC from the perspective of Shaoyang pattern,which is very distinctive.4.Some usual methods of treating PIC by professor Liu Chenggang can be specifically expressed as follows: when external pathogens lead to a lung disease,different curative methods should be chosen according as the external pathogen is cold or warm;when internal causes result in a lung disease,different curative methods should be adopted according as the patterns are deficient or excessive;a curative method of reconciling Shaoyang is used to restore the free circulation of qi and blood for human body;Five-Phase theory is also utilized to treat PIC on the whole.
Keywords/Search Tags:Postinfectious cough, Data Mining, Empirical Research
PDF Full Text Request
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