Exploring the Medication Patterns of Professor Lu Guangrong in Treating Insomnia with Traditional Chinese Medicine Based on Data Mining
Objective To conduct an in-depth analysis of the medication patterns in the treatment of insomnia by Professor Lu Guangrong using traditional Chinese medicine(TCM)through data mining techniques.Methods Clinical data from all insomnia patients treated by Professor Lu Guangrong at the outpatient clinic of Yunnan Province Traditional Chinese Medicine Hospital were collected.The data were entered into a prescription database established in Microsoft Excel,and R software version 4.3.1 was used for frequency statistics of the medications,analysis of properties and meridian tropism,and association rule analysis.Results A total of 336 prescriptions that met the criteria were included,involving 175 Chinese medicinal herbs.The most frequently used herbs were Poria(Fu Ling),Atractylodes(Bai Zhu),Salvia miltiorrhiza(Dan Shen),Licorice(Gan Cao),and Spine Date Seed(Suan Zao Ren),with Poria being the most frequently used(281 times).The distribution of the properties of the herbs was:warm＞neutral＞cold＞cool＞hot,with warm(43.70％)being predominant;the flavor distribution was:sweet＞pungent＞bitter＞sour,with sweet flavors being most common(39.65％);the herbs mainly targeted the Spleen,Lung,and Liver meridians,with the Spleen meridian being the primary target(20.45％).The most frequent co-occurrence was Spine Date Seed and Poria(223 times),and the optimal number of clusters was three,indicating high correlation among three groups of medicines.Conclusion In the treatment of insomnia,Professor Lu Guangrong adheres to the academic thought of disease first and syndrome secondly,and advocates the treatment principle of adjusting to individual conditions,making up the treatment principle of clarifying and mild,so as to restore the balance of Yin and Yang,the regulation of Ying and Wei,and the safety of five Zang organs.
insomniatraditional Chinese medicinedata miningrenowned traditional Chinese medicine expertsR language
NETL
NSTL
万方数据
