Institute of Chinese Materia Medica, China Academy of Traditional Chinese Medicine, Beijing 100700, China.
J Tradit Chin Med. 2010 Dec;30(4):288-93. doi: 10.1016/s0254-6272(10)60058-1.
To analyze the component law of Chinese patent medicines for anti-influenza and develop new prescriptions for anti-influenza by unsupervised data mining 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.
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.
Unsupervised data mining methods are able to mine the component law quickly and develop new prescriptions.
分析治疗流感的中药成方规律，采用无监督数据挖掘方法开发治疗流感的新处方。
收集治疗流感的中药成方，录入数据库，分别采用改进互信息法、复杂系统熵聚类和无监督层次聚类对药物、药物组合和新处方进行相关性分析。
基于对 126 个中药成方的分析，确定了各药味在处方中的使用频次、54 对常用药对、34 个核心组合，并开发出 4 个流感新处方。
无监督数据挖掘方法能够快速挖掘出药物组成规律，开发新处方。
