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[基于中医功能组合确定四性的挖掘规则]

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

作者信息

Yang Xue-Mei, Lin Duan-Yi, Lai Xin-Mei, Chen Mei-Mei, Huang Lu-Qi

机构信息

Fujian University of Traditional Chinese Medicine, Fuzhou 350108, China.

出版信息

Zhongguo Zhong Yao Za Zhi. 2013 May;38(10):1624-6.

Abstract

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 rules, 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, warming 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.

摘要

目的

为全面总结中医四性理论奠定大数据挖掘基础,也为中药新资源开发及中药临床应用的四性判定提供理论线索。

方法

选取《中华本草》中8980味中药的四性数据及相关功效指标数据作为数据集。采用IBM SPSS Clementine 14.1数据挖掘平台及Apriori模型挖掘分类关联规则,将规则前项的最小支持阈值和最小置信阈值分别设定为0.5%和60%。

结果

发现11条涉及温、寒、平性的分类关联规则。

结论

发现具有“祛风散寒、温中止痛、祛风除湿、补肾壮阳、通络祛风除湿、散寒止痛、强筋健骨、祛风除湿”功效的中药可能为温性,具有“补肺”功效的中药可能为平性,具有“清热燥湿、清热泻火、除烦”功效的中药可能为寒性。

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