摘要
针对在药房和药店等医疗保健系统产生大量的交易数据进行数据挖掘的问题。为了从药房和药店等医疗保健系统中获取有关药物间关联的有用信息,在本文中,采用改进Apriori算法从药房内订购的处方获得的数据进行数据挖掘,通过SPSS Clementine平台试验,从这些处方中的指定药物中获得了10个关联规则。在这些关联规则中,得出了以下主要结论:维生素D和钙片是最相关的药物,奥美拉唑和甲硝唑在关联方面排名第二。这些规则的准确性也由医生亲自研究和审查。
The problem of data mining is that large amounts of transaction data are generated in health care systems such as pharmacies and pharmacies.In order to obtain useful information about drugassociation from health care systems such as pharmacies and pharmacies,in this paper,data mining is performed using improved Apriori algorithms to obtain data from prescriptions ordered in pharmacies,from the SPSS Clementine platform trials,from these prescriptions.10 association rules were obtained for the specified drugs.In these association rules,the following main conclusions are drawn:Vitamin D and calcium tablets are the most relevant drugs,and omeprazole and metronidazole rank second in association.The accuracy of these rules is also studied and reviewed by doctors personally.
作者
黄黎明
刘振宇
HUANG Li-ming;LIU Zhen-yu(School of Computer,University of South China,Hengyang 421001,China)
出处
《电子设计工程》
2018年第24期36-40,共5页
Electronic Design Engineering
关键词
数据挖掘
关联规则
购买组合分析
算法
data mining
association rules
purchase portfolio analysis
algorithm
作者简介
黄黎明(1991—),男,湖南衡阳人,硕士研究生。研究方向:数据挖掘、医学信息工程。
