Literature Research And Data Mining Based On Stuffiness And Fullness Medical Cases Of Famous Ancient Scholars

Objective The article is to summarize the treatment characteristics and application of classical prescriptions through sorting out the medical cases of stuffiness and fullness of ancient famous scholars.Combined with the theoretical knowledge of traditional Chinese medicine,analyzed the rules of prescription medicine,and provided reference and broadened the thinking of diagnosis and treatment for modern clinical treatment of stuffiness and fullness.Methods 1.On the basis of consulting and investigating the relevant documents of the medical records of stuffiness and fullness in ancient medical books,taking the books collected by the library of Zhejiang University of traditional Chinese medicine and the library of Zhejiang Research Institute of traditional Chinese medicine and the developed electronic ancient books as the data source,consulting the representative works of stuffiness and fullness medical records of all generations before the end of Qing Dynasty(1911),and referring to the large-scale literature "medical records clustering",selecting the medical cases with the main symptoms of stuffiness and fullness.As the key research object,the literature uses the principles of literature research to eliminate the false and to preserve the true,to eliminate the coarse and to preserve the fine,and to make the past serve the present,and sums up and analyzes the characteristics of the treatment methods and commonly used prescriptions of the stuffiness and fullness medical case.2.To standardize the data of the prescriptions of medical cases that meet the inclusion criteria,input them into the software of traditional Chinese Medicine Inheritance auxiliary system(v2.5)system,and establish a prescription database for the treatment of stuffiness and fullness.The frequency statistics,rule analysis,complex system entropy clustering and other data mining methods integrated by the software were used to make statistics of high-frequency medicines,medicine properties,and so on.So as to comprehensively and deeply excavate the characteristics and rules of the treatment of stuffiness and fullness medical cases in ancient Chinese books.Results ①In ancient Chinese medical books,there are eight methods to treat stuffiness and fullness,such as reconciling,warming and tonifying,nourishing Yin,removing dampness,eliminating phlegm,dispersing lung qi,lifting and removing accumulation.Commonly used prescriptions include Banxia Xiexin Decoction,Xuanfu Daizhe Decoction,Lizhong decoction,Lingguizhugan decoction,gardenia black bean soup,Xiaoxianxiong Decoction and so on.②There are 290 medical cases and 390 prescriptions,involving 266 traditional Chinese medicines,with a total frequency of 3196 times.The results of data mining show that the top five drugs used in the treatment of stuffiness and fullness are Orange peel,Tuckahoe,Pinellia,Atractylodes macrocephala and Ginseng;According to the classification and statistics of high-frequency drugs,the most commonly used traditional Chinese medicine is Qi regulating medicine,followed by tonifying deficiency medicine,resolving phlegm,relieving cough and asthma,promoting water and dampness,and warming internal medicine;The four kinds of drugs are mainly warm,followed by flat;the five kinds of drugs are mainly pungent,followed by bitter;the medicine flavor is mainly spleen and stomach,and also closely related to lung meridian;Pinellia ternata and Poria cocos are the most frequently used drug combinations based on association rules;many famous prescriptions were contained in high-frequency drugs,such as Erchen decoction,Sijunzi Decoction,Lizhong decoction,Zuojin Pill,Banxia Xiexin Decoction,Pingwei Powder;12 new prescriptions for the treatment of stuffiness and fullness were obtained.Conclusion The main treatment methods and drug use rules based on data mining in ancient medical records of stuffiness and fullness can be used for reference in modern clinical diagnosis and treatment of stuffiness and fullness.