Open AccessAnalysis of Drug Use Rules of Traditional Chinese Medicine for Dysmenorrhea Based on Data Mining
Author(s) -
Li Bai,
Xiang-Ping Wei,
Mingsan Miao
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/295/3/032010
Subject(s) - traditional chinese medicine , tian , medical prescription , medicine , association rule learning , traditional medicine , angelica sinensis , alternative medicine , safer , data mining , computer science , pharmacology , statistics , mathematics , art , literature , pathology
Objective: Based on data mining, traditional Chinese medicine is used to treat dysmenorrhea. Methods: using the journal literatures collected by cnki as the data source and using the software of Excel 2013, SPSS Modeler 14.1 and SPSS Statistics 19.0, the association rule analysis and factor analysis of the included standard Chinese medicine were carried out. Results: among the 111 prescriptions included in the standard, angelica sinensis and Corydalis rhizoma were the most common. The medicinal property is mainly warm and smooth, and the bitterness and bitterness are the main ingredients, Meridian of liver in the majority. In association rule analysis, 10 combinations of drug pairs with the highest association strength were found, and 5 common factors were extracted from factor analysis. Conclusion: By using modern information technology and combining TCM clinical data with big data for in-depth analysis and integration, the potential compatibility rules of TCM for dysmenorrhea treatment can be more easily explored and finally fed back to the clinic, providing a basis for clinical safer and more rational drug use and new prescription research and development.