Based On Data Mining, This Paper Discusses Professor You Zhaoling's Medication Rules For The Treatment Of Low Ovarian Response

Objective:Based on the traditional teacher training,this research explores Professor You Zhaoling's clinical diagnosis and treatment of poor ovarian response by using data mining techniques,frequency analysis,association rules analysis,cluster analysis and principal component analysis.To summarize Professor You's medication experience and clinical thinking,to explore her academic thought,in order to provide reference for the inheritance and innovation of future generations.Method:This topic by collecting July 1,2015 to December 31,2015 Professor You Zhaoling poor ovarian response outpatient cases,in strict accordance with the inclusion criteria and exclusion criteria were screened,a total of 52 cases that meet the standard criteria were selected and a total of 200 Chinese medicine prescriptions were involved.With reference to "traditional Chinese medicine" teaching materials and "Chinese Dictionary" on Chinese medicine name,efficacy,medicine,naturalization,medicine and other standardized standard processing,the Chinese name data into a classification variable.Application of Microsoft Excel 2007 on Windows7 platform to establish Professor You Zhaoling diagnosis and treatment of poor ovarian response database,the use of Microsoft Excel 2007 for general information and frequency analysis,the use of IBM SPSS Statistics 22 to the database frequency is greater than 10%(frequency greater than 20 times)High-frequency Chinese medicine was clustered to analyze association rules and principal component analysis of high-frequency core Chinese medicinewith a frequency of more than 30%(frequency greater than 60 times)in the database using IBM SPSS Modeler 18.1.The results obtained were combined with the theories of modern medicine and traditional Chinese medicine Analysis and discussion.Result:1.General data analysis:The average age of participants was 34.21 ±5.60 years old,the maximum age of 45 years old,the youngest age of 23years;the maximum number of patients admitted to treatment was 3 times and 4 times,each 18 cases,accounting for 34.62%.Among the participants,the number of implants in the evaluation of efficacy was the highest,43cases(82.69%).2.Frequency analysis:52 cases that meet the standard cases,involving200 Chinese medicine prescriptions,including 91 kinds of traditional Chinese medicine,a total of 3301 frequency of all drugs,of which frequency of more than 10 in 65 kinds of traditional Chinese medicine,the first 10 followed by astragalus(179 times with frequency of 89.5%),Atractylodes(179 times with frequency of 89.5%),Codonopsis(179 times with frequency of 89.5%),Licorice(174 times with frequency of 87.0%),Pueraria(144 times with frequency of 72.0%(104 times with frequency of52.0%),yam(103 times with frequency of 51.5%),dodder(95 times with frequency of 47.5%),orange leaves with 92 times with frequency of 46.0%,Panax notoginseng with frequency of 45.5%).Drug efficacy analysis found that involving a total of 13 types of Chinese medicine,of which the most use of tonic drugs,including 29 kinds of drugs,a total of 1548 times,accounting for 46.9% of the total drugs,followed by heat-clearing drugs.Frequency analysis of tonic drugs,found that the use of up to the gas category,including nine kinds of drugs,a total of 896 times.A total of 58 kinds of sweet and sour herbs were used in the analysis of medicinal properties,involving a total of 6 kinds of medicinal herbs,accounting for2396 times,accounting for 72.6%.In the analysis of drugs found that involved 12,the most traditional Chinese medicine to the spleen,a total of30 flavors,accounting for 1669 times,accounting for 50.6%.Pharmacological analysis found a total of 4 types of medicinal properties,the use of the most flat drug,a total of 27 flavors,accounting for 1256 times,accounting for 38.0%.3.Association rules: According to the Apriori algorithm,the minimum support is 45.0%,the minimum confidence is 80.0%,the maximum number of the previous 5,resulting in a total of 77 rules.4.Clustering analysis: According to the Q-type cluster analysis using the distance coefficient statistics and the TCM theory,the following 10 clusters were obtained: Fritillaria cirrhosa,Smilax glabra;Maca,black soybean;Polygonatum,raspberry;Day,Gynostemma;Codonopsis,Astragalus,Atractylodes;Folium,forsythia,Prunella;Radix,dandelion,Viola;Rosa laevigata,pomegranate peel,plum;Angelica,Chuanxiong,Zeeland,Alisma,Motherwort;lotus seed,Yam,dodder,mulberry,Polygonatum,medlar.5.Principal component analysis: According to the load factor larger than 0.5,determine the corresponding drugs for each factor,and draw the following five factors: lotus seeds,yam,dodder,orange leaves,incense,mulberry,daqingye,forsythia,Huang Jing,medlar,Prunella,Dendrobium;Astragalus,Atractylodes,Codonopsis;Panax notoginseng;Pueraria;Motherwort.In conclusion:Through the study found that Professor You Zhaoling in clinicaldiagnosis and treatment of poor ovarian response process,that POR patients with multiple spleen and kidney deficiency,clinical evidence of spleen and kidney,peace of mind,according to the characteristics of the woman's womb bleeding,according to the menstrual period and Follicular growth stage staged treatment.In the medication on the most common use of tonic,but there is no lack of heat type,qi,blood circulation and blood type drugs,embodies the characteristics of Professor Yu tackling both.And Professor You prescription overall is partial to the peace of mind to maintain the treatment of yin and yang,through the appropriate compatibility of cold and heat,so Ping Bu ping attack,make up instead of stagnation,clear without injury,moistening and mutual aid.The subject for the first time using data mining techniques Professor You Zhaoling regulating poor ovarian response analysis of Chinese medicine,premature ovarian cancer patients to explore the law of drugs,inheritance of Professor Yu experience in clinical research to provide new ideas,but there are still A certain lack of.