Complex Systems Entropy Network and Its Application in Data Mining for Chinese Medicine Tumor Clinics

World Science and Technology Pub Date : 2012-04-01 DOI:10.1016/S1876-3553(12)60038-6

Yang Ming , Jiao Lijing , Chen Peiqi , Wang Jue , Xu Ling

{"title":"Complex Systems Entropy Network and Its Application in Data Mining for Chinese Medicine Tumor Clinics","authors":"Yang Ming , Jiao Lijing , Chen Peiqi , Wang Jue , Xu Ling","doi":"10.1016/S1876-3553(12)60038-6","DOIUrl":null,"url":null,"abstract":"<div><p>This study was aimed at investigating the method of data mining for complex Chinese medicine tumor clinical data. This article introduces a complex systems entropy network for data mining in tumor clinics. A mutual information algorithm based on the random permutation test was proposed for assessing the correlation of multi-variables, and a complex network was established. Based on the tumor clinical data (718 cases) collected, data mining was performed with the help of the statistical information of the complex network. The results showed that interaction effects among multi-variables were discovered by a complex systems entropy network. A total of 116 pairs of synergy variables and 14 core synergy cliques, 82 pairs of antagonistic variables and 7 core antagonistic cliques were found after 718 cases of data mining. The results, for the most part, correspond with the actual clinics. It was concluded that the complex systems entropy network is suitable for the analysis of interaction effects among multi-variables and also for data mining in complex Chinese medicine clinics.</p></div>","PeriodicalId":101287,"journal":{"name":"World Science and Technology","volume":"14 2","pages":"Pages 1376-1384"},"PeriodicalIF":0.0000,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1876-3553(12)60038-6","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1876355312600386","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 8

Abstract

This study was aimed at investigating the method of data mining for complex Chinese medicine tumor clinical data. This article introduces a complex systems entropy network for data mining in tumor clinics. A mutual information algorithm based on the random permutation test was proposed for assessing the correlation of multi-variables, and a complex network was established. Based on the tumor clinical data (718 cases) collected, data mining was performed with the help of the statistical information of the complex network. The results showed that interaction effects among multi-variables were discovered by a complex systems entropy network. A total of 116 pairs of synergy variables and 14 core synergy cliques, 82 pairs of antagonistic variables and 7 core antagonistic cliques were found after 718 cases of data mining. The results, for the most part, correspond with the actual clinics. It was concluded that the complex systems entropy network is suitable for the analysis of interaction effects among multi-variables and also for data mining in complex Chinese medicine clinics.

查看原文本刊更多论文

复杂系统熵网络及其在中医肿瘤临床数据挖掘中的应用

本研究旨在探讨复杂中医肿瘤临床数据的数据挖掘方法。本文介绍了一种用于肿瘤临床数据挖掘的复杂系统熵网络。提出了一种基于随机排列检验的互信息算法来评估多变量之间的相关性，并建立了复杂网络。以收集到的718例肿瘤临床数据为基础，利用复杂网络的统计信息进行数据挖掘。结果表明，通过复杂系统熵网络发现了多变量间的交互作用。通过718例数据挖掘，共发现协同变量116对，核心协同集团14对;拮抗变量82对，核心拮抗集团7对。结果，在很大程度上，符合实际的诊所。结果表明，复杂系统熵网络适用于多变量交互效应分析和复杂中医临床数据挖掘。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

World Science and Technology

自引率

0.00%

发文量