Darmoni Stefan J, Névéol Aurelie, Renard Jean-Marie, Gehanno Jean-Francois, Soualmia Lina F, Dahamna Badisse, Thirion Benoit
CISMeF, Rouen University Hospital, 76031 Rouen, France.
BMC Med Inform Decis Mak. 2006 Feb 7;6:7. doi: 10.1186/1472-6947-6-7.
Categorization is designed to enhance resource description by organizing content description so as to enable the reader to grasp quickly and easily what are the main topics discussed in it. The objective of this work is to propose a categorization algorithm to classify a set of scientific articles indexed with the MeSH thesaurus, and in particular those of the MEDLINE bibliographic database. In a large bibliographic database such as MEDLINE, finding materials of particular interest to a specialty group, or relevant to a particular audience, can be difficult. The categorization refines the retrieval of indexed material. In the CISMeF terminology, metaterms can be considered as super-concepts. They were primarily conceived to improve recall in the CISMeF quality-controlled health gateway.
The MEDLINE categorization algorithm (MCA) is based on semantic links existing between MeSH terms and metaterms on the one hand and between MeSH subheadings and metaterms on the other hand. These links are used to automatically infer a list of metaterms from any MeSH term/subheading indexing. Medical librarians manually select the semantic links.
The MEDLINE categorization algorithm lists the medical specialties relevant to a MEDLINE file by decreasing order of their importance. The MEDLINE categorization algorithm is available on a Web site. It can run on any MEDLINE file in a batch mode. As an example, the top 3 medical specialties for the set of 60 articles published in BioMed Central Medical Informatics & Decision Making, which are currently indexed in MEDLINE are: information science, organization and administration and medical informatics.
We have presented a MEDLINE categorization algorithm in order to classify the medical specialties addressed in any MEDLINE file in the form of a ranked list of relevant specialties. The categorization method introduced in this paper is based on the manual indexing of resources with MeSH (terms/subheadings) pairs by NLM indexers. This algorithm may be used as a new bibliometric tool.
分类旨在通过组织内容描述来增强资源描述，以便读者能够快速轻松地掌握其中讨论的主要主题。这项工作的目的是提出一种分类算法，用于对用医学主题词表（MeSH）索引的一组科学文章进行分类，特别是对MEDLINE书目数据库中的文章进行分类。在诸如MEDLINE这样的大型书目数据库中，找到特定专业组特别感兴趣的材料或与特定受众相关的材料可能很困难。分类可优化索引材料的检索。在CISMeF术语中，元术语可被视为超级概念。它们最初是为了提高CISMeF质量控制的健康网关中的召回率而构思的。
MEDLINE分类算法（MCA）一方面基于MeSH术语与元术语之间以及MeSH副标题与元术语之间存在的语义链接。这些链接用于从任何MeSH术语/副标题索引中自动推断出元术语列表。医学图书馆员手动选择语义链接。
MEDLINE分类算法按相关医学专业的重要性降序列出与MEDLINE文件相关的医学专业。MEDLINE分类算法可在网站上获取。它可以以批处理模式在任何MEDLINE文件上运行。例如，目前在MEDLINE中索引的BioMed Central Medical Informatics & Decision Making上发表的60篇文章中，排名前三的医学专业是：信息科学、组织与管理以及医学信息学。
我们提出了一种MEDLINE分类算法，以便以相关专业的排名列表形式对任何MEDLINE文件中涉及的医学专业进行分类。本文介绍的分类方法基于美国国立医学图书馆（NLM）索引员使用MeSH（术语/副标题）对资源进行的手动索引。该算法可作为一种新的文献计量工具。
