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A method for mining the empirical formula based on Apriori algorithm

机译:一种基于Apriori算法的经验公式挖掘方法

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Objective: Based on the Apriori algorithm, we develop a method for mining traditional Chinese medicine herb prescriptions. Procedures and Methods: input the 1,632 medical records of Chen Shouqiang, Associate Chief Physician, into the electronic medical records management system; mine the high-frequency combination of traditional Chinese medicine for treatment of coronary heart disease via the Apriori algorithm; take the combination with most kinds of herbal medicines as the motherboard, and then add Chinese medicine different from what included in the top N groups of high-frequency combinations of traditional Chinese medicine; thus we can sum up the empirical formula. Results: the empirical formula mined consists of 12 herbs (i.e. rhizome of Ligusticum wallichii, salvia miltiorrhiza, tuber of dwarf lilyturf, Costustoot, licorice Roots Northwest Origin, Schisandra chinensis, Coptis chinensis, Scutellaria baicalensis, charred triplet, fructus forsythiae, cuttlebone and radix astragali), which is similar with the chest stuffiness No. 2 formula commonly used in clinic. Conclusion: This method has some significance in the experience heritage of distinguished veteran doctors of TCM, and it is worth promoting.
机译:目的:基于Apriori算法,开发一种中草药配方的提取方法。程序和方法:将副主任医师陈寿强的1,632份病历输入电子病历管理系统;通过Apriori算法挖掘高频中药组合治疗冠心病的方法;以与大多数草药的组合为母板,然后添加不同于前N组高频中药组合中所含中药的中药;因此我们可以总结出经验公式。结果:提取的经验公式包括12种草药(即女贞的根茎,丹参,矮小百合的块茎,木薯块茎,西北甘草根,五味子,黄连,黄连,黄char,cut三叶草,连翘黄芪),与临床上常用的2号胸闷配方相似。结论:该方法对中医优秀老医师的经验传承具有一定的意义,值得推广。

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