Multilabel associative classification categorization of MEDLINE articles into MeSH keywords - An intelligent data mining technique to more accurately classify large volumes of documents
Multilabel associative classification categorization of MEDLINE articles into MeSH keywords - An intelligent data mining technique to more accurately classify large volumes of documents
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DOI：
10.1109/memb.2007.335581
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发表时间：
2007-03-01
影响因子：
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通讯作者：
Reformat, Marek
中科院分区：
文献类型：
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作者：
Rak, Rafal;Kurgan, Lukasz A.;Reformat, Marek
近年来，NLM的工具和数据库引起了人们的极大关注。生物医学文档的文本分析和知识挖掘(MedTAKIMI)是一个应用程序，用于促进从非常大的文本数据库(如MEDLINE)中发现知识，已在[3]中开发和描述。根据作者的说法，该应用程序动态挖掘文档以获取其特征特征，并使用诸如网格关键字等类别进行术语提取和交互式向下钻取系列查询。另一个名为MedMeSH摘要管理器的应用程序使用Mesh关键字来注释从DNA微阵列获得的一组基因，方法是汇总与用户定义查询中的基因相关的MEDLINE文章引用的所有术语[4]。探索用于表示文本的特征之间的关系，并将其应用于MEDLINE，已在[5]中进行了研究。该方法使用关联规则，比较了三个不同的语义级别：单词、网格关键词和来自NLM的统一医学语言系统(UMLS)的自动选择的概念。作者对这三个水平的可信性和有用性特别感兴趣。在我们的研究中，我们使用了OHSUMED，这是MEDLINE数据库的语料库子集。该集合还被许多研究人员用来使用网格进行分类
NLM’s tools and databases have attracted significant attention in recent years. The Text Analysis and Knowledge Mining for Biomedical Documents (MedTAKIMI), which is an application to facilitate knowledge discovery from very large text databases such as the MEDLINE, has been developed and described in [3]. According to the authors, the application dynamically mines documents to obtain their characteristic features and uses categories such as MeSH keywords for term extraction and interactive series of drill-down queries. Another application, called MedMeSH Summarizer, uses MeSH keywords to annotate a set of genes obtained from DNA microarrays by summarizing all the terms tagged to MEDLINE article references that are related to a gene in a user-defined query [4]. Exploration of relationships between features used to represent text, with application to MEDLINE, has been studied in [5]. The method uses association rules and compares three different semantic levels: words, MeSH keywords, and automatically selected concepts coming from NLM’s Unified Medical Language System (UMLS). The authors were especially interested in plausibility and usefulness of the three levels. In our research we use OHSUMED, a corpus subset of the MEDLINE database. This collection has been also used by many researchers to perform classification using MeSH