Analysis on the principle of the drug use ofMenghe physiciansMa-Peizhi based on apriori and clustering algorithm
10.3760/cma.j.issn.1673-4246.2014.10.015
- VernacularTitle:基于数据挖掘的孟河名医马培之用药规律研究
- Author:
Weixian GUO
;
Jiarui WU
;
Xiaomeng ZHANG
;
Bing YANG
;
Mengdi ZHAO
;
Xiuqin HUANG
;
Bing ZHANG
- Publication Type:Journal Article
- Keywords:
Ma-Peizhi;
Data mining;
Association Rules;
Clustering Algorithm
- From:
International Journal of Traditional Chinese Medicine
2014;(10):916-919
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the principle of the drug use ofMenghe PhysiciansMa-Peizhi by using the Traditional Chinese Medicine(TCM)inheritance support system.Methods The prescriptions for the commonly encountered diseases that used byMa-Peizhi were collected to build a database, and analyze by the unsupervised data mining methods, such as apriori algorithm, entropy clustering complex systems, from which we could get the frequency of the drugs, the association rules between drugs, the core drug combinations, and so on.Results Based on the analysis of 745 prescriptions, the most frequently used drugs were tuckahoe, chiretta, paenoiae alba, dried orangepeel and dioscoreae. The core drug combinations were “radix rehmanniae recen- salivia chinensis-ophiopogon root”, “teasel root-viscum album-achyranthes”, “menispermaceae-heracleum hemsleyanum michaux-gentiana macrophylla”, and “mulberry leaf-periostracum cicadae-the root of balloon flower”. The new prescriptions were “mulberry leaf-viter rotundifolia-batryticated silkworm-periostracum cicadae-the root of balloon flower”, “teasel root-viscum album- achyranthes- ramulus mori- periplocae”, and so on.ConclusionMenghe PhysiciansMa-Peizhi was well experienced in treating the commonly encountered diseases by agile diagnosis and treatment, and addition or subtraction of changes based on the classical prescriptions.
