探索来自医学文献数据库（MEDLINE）的文本挖掘。
Exploring text mining from MEDLINE.
作者信息
Srinivasan Padmini, Rindflesch Thomas
机构信息
University of Iowa, Iowa City, IA 52242, USA.
出版信息
Proc AMIA Symp. 2002:722-6.
We present a text mining application that exploits the MeSH heading subheading combinations present in MEDLINE records. The process begins with a user specified pair of subheadings. Co-occurring concepts qualified by these subheadings are regarded as being conceptually related and thus extracted. A parallel process using SemRep, a linguistic tool, also extracts conceptually related concept pairs from the titles of MEDLINE records. The pairs extracted via MeSH and the pairs extracted via SemRep are compared to yield a high confidence subset. These pairs are then combined to project a summary view associated with the selected subheading pair. For each concept the "diversity" in the set of related concepts is assessed. We suggest that this summary and the diversity indicators will be useful a health care practitioner or researcher. We illustrate this application with the subheading pair "drug therapy" and "therapeutic use" which approximates the treatment relationship between Drugs and Diseases.
我们展示了一个文本挖掘应用程序，该程序利用了MEDLINE记录中存在的医学主题词（MeSH）标题副标题组合。该过程始于用户指定的一对副标题。由这些副标题限定的同时出现的概念被视为在概念上相关，因此被提取出来。使用语言工具SemRep的并行过程也从MEDLINE记录的标题中提取概念上相关的概念对。将通过MeSH提取的概念对与通过SemRep提取的概念对进行比较，以产生一个高置信度子集。然后将这些概念对组合起来，以呈现与所选副标题对相关的汇总视图。对于每个概念，评估相关概念集中的“多样性”。我们认为，这个汇总和多样性指标对医疗从业者或研究人员将是有用的。我们用副标题对“药物疗法”和“治疗用途”来说明这个应用程序，这近似于药物与疾病之间的治疗关系。
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