Research on Component Law of Chinese Patent Medicine for Anti-influenza and Development of New Recipes for Anti-influenza by Unsupervised Data Mining Methods

被引：16

作者：

Tang Shi-huan ^{[1
]}

Chen Jian-xin ^{[2
]}

Li Geng ^{[3
]}

Wu Hong-wei ^{[1
]}

Chen Chang ^{[1
]}

Zhang Na ^{[4
]}

Gao Na ^{[4
]}

Yang Hong-jun ^{[1
]}

Huang Lu-qi ^{[1
]}

机构：

[1] China Acad Tradit Chinese Med, Inst Chinese Mat Med, Beijing 100700, Peoples R China

[2] Beijing Univ Tradit Chinese Med, Beijing 100029, Peoples R China

[3] Minist Hlth PRC, China Japan Friendship Hosp, Beijing 100029, Peoples R China

[4] N China Coal Med Univ, Tangshan 063000, Peoples R China

来源：

JOURNAL OF TRADITIONAL CHINESE MEDICINE | 2010年 / 30卷 / 04期

关键词：

influenza; unsupervised data mining methods; swine influenza; new prescription discovery;

D O I：

10.1016/S0254-6272(10)60058-1

中图分类号：

R [医药、卫生];

学科分类号：

10 ;

摘要：

Objective: To analyze the component law of Chinese patent medicines for anti-influenza and develop new prescriptions for anti-influenza by unsupervised data mining methods. Methods: Chinese patent medicine recipes for anti-influenza were collected and recorded in the database, and then the correlation coefficient between herbs, core combinations of herbs and new prescriptions were analyzed by using modified mutual information, complex system entropy cluster and unsupervised hierarchical clustering, respectively. Results: Based on analysis of 126 Chinese patent medicine recipes, the frequency of each herb occurrence in these recipes, 54 frequently-used herb pairs, 34 core combinations were determined, and 4 new recipes for influenza were developed. Conclusion: Unsupervised data mining methods are able to mine the component law quickly and develop new prescriptions.

引用

页码：288 / 293

页数：6

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