摘要
为了从临床数据中挖掘出疾病之间的相关性,为疾病临床诊断提供一种辅助方法,使用SPSS Statistics进行数据预处理,将数据转化为布尔数据,最后应用SPSS Modeler搭建基于Apriori算法的关联规则挖掘数据流,采用云南某医院2013年住院病案首页数据(共54 841条)建立疾病间的关联规则模型。从227种疾病中挖掘出信度大于20%的关联规则共40条,涉及20种疾病。关联规则挖掘可以从大量临床数据中发现疾病间潜在关联,为相关疾病的临床诊断提供辅助。
In order to dig out the correlation between diseases from clinical data, an auxiliary method is provided for the clinical diagnosis of disease. SPSS Statistics is used to preprocess the data and convert the data into Boolean data. Finally, SPSS Model- er is applied to build association rules mining data stream based on Apriori algorithm. An association rule model between disea- ses was established by using the first page of inpatient medical records（a total of 54 841） in a hospital in Yunnan in 2013. A total of 40 association rules with confidence setting greater than 20% were extracted from the 227 diseases, involving 20 diseases. Association rules mining can discover the latent association between diseases from a large amount of clinical data. This can pro- vide an auxiliary method for the clinical diagnosis of related diseases.
出处
《软件导刊》
2018年第3期162-164,共3页
Software Guide
基金
国家自然科学基金项目(11265007)
