摘要
目的:应用频数分析和聚类分析方法对小柴胡汤类方应用医案进行数据挖掘,探索小柴胡汤类方"证-病-方-药"之间的多维内在规律。方法:以《中医大辞典·方剂分册》为方剂来源,收集有关小柴胡汤类方的方剂,以频数分析为切入点,引入黄金分割法,统计分析高频症状、主治病名、药物、药物功效,并对高频药物进行聚类分析。结果:在收集到的509首方剂中,涉及151种症状,共3 243频次,以发热为首的高频症状11种;涉及127种疾病名称,共534次,以外感热病为首的高频主治病名4种;涉及178味药物,共4 283频次,以柴胡为首的高频药15味;按药物功效分共18类,以解表药为首;对高频药物进行聚类分析,形成2个聚类方。结论:频数分析与聚类分析能够客观的反映小柴胡汤类方应用医案处方用药的配伍规律。
Objective： Adopt the frequency analysis and cluster analysis method to conduct the data mining,in order to explore the multi-dimensional inherent law for syndromes,prescriptions and Chinese herbs in ZHANG Zhongjing＇s famous prescription Xiaochaihu Tang and its related prescriptions. Method： Xiaochaihu Tang and its related prescriptions were collected from the Volume of Prescriptions of the Dictionary of Traditional Chinese Medicine. The golden section method was used to make a frequency analysis on high-frequency symptoms,disease names,Chinese herbs,Chinese herbs＇ efficacy,and cluster analysis on high-frequency Chinese herbs. Result：The 509 prescriptions collected involved 151 symptoms,which appeared for 3 243 times,including 11 highfrequency symptoms,with fever as the dominance. They involved 127 disease names,which appeared for 543 times,including 4 high-frequency disease names,with exogenous febrile disease as the dominance. They involved178 herbs,which appeared for 4 283 times,including 15 high-frequency herbs,with Bupleuri Radix as the dominance. These prescriptions can be classified into 18 categories according to their efficiency,with those relieving exterior syndromes as the dominance. Two clustering prescriptions were obtained through the cluster analysis on high-frequency herbs. Conclusion： The frequency analysis and the cluster analysis can objectively reflect the compatibility law of prescriptions related to Xiaochaihu Tang.
出处
《中国实验方剂学杂志》
CAS
CSCD
北大核心
2016年第21期186-190,共5页
Chinese Journal of Experimental Traditional Medical Formulae
基金
国家自然科学基金项目(81573990)
川北医学院科研发展计划重点培育项目(CBY13-B-ZP01)
关键词
小柴胡汤类方
数据挖掘
频数分析
聚类分析
配伍规律
prescriptions related to Xiaochaihu Tang
data mining
frequency analysis
cluster analysis
compatibility law
