挖掘中药功能组合判定四气药性的规律

Mining rules on determination of four properties based on traditional Chinese medicine functional combination

目的:为中药药性四气理论的全面总结奠定大样本数据挖掘的基础,并为中药新资源的开发及临床用药提供四气药性判定的理论线索。方法:选择《中华本草》所载8 980味中药的四气数据及关联的药物功能索引数据作为数据集,采用IBM SPSS Clementine 14.1数据挖掘平台,选择Apriori模型挖掘分类关联规则,设置规则前件最小支持度阈值为0.5%,最小置信度阈值为60%。结果:共挖掘出涉及温、寒、平三性的分类关联规则11条。结论:具有发散风寒、温中、止痛&祛风除湿、补肾阳、通经&祛风除湿、散寒止痛、强筋骨&祛风除湿功能的中药其药性多属"温";具有补肺功能的中药其药性多属"平";具有清热燥湿、清热泻火、除烦功能的中药其药性多属"寒"。

Objective： It laid the foundation of the large sample data mining for a comprehensive summary concerning four properties theory of traditional Chinese medicine （TCM）, and also provided theory clues on determination of four properties for the new resource development of TCM and the clinical use of Chinese medicine. Method： Four properties data of 8 980 Chinese medicines from ＂Chinese herbal medicine （CHM） ＂ and associated function index data were chose as data sets. Then, the IBM SPSS Clementine 14. 1 data mining platform and Apriori model were adopted to mining classification-association rides, setting the minimum support threshold of rule antecedent and the minimum confidence threshold as 0. 5% and 60%. Result： 11 classification-association rules involved in warm, cold and mild natures were found. Conclusion： It was discovered that the TCM with functions of ＂dispelling wind-cold, war- ming the middle, stopping pain and expelling wind-removing dampness, tonifying kidney yang, unblocking meridians and expelling wind-removing dampness, dispelling cold to stop pain, strengthening sinews-bones and expelling wind-removing dampness＂ was likely warm-natured, with functions of ＂tonifying the lung＂ was likely mild-natured, and with functions of ＂ clearing heat and drying dampness, clearing heat and purging fire, eliminating restlessness＂ was likely cold-natured.