A recent advance in the automatic indexing of the biomedical literature

The volume of biomedical literature has experienced explosive growth in recent years. This is reflected in the corresponding increase in the size of MEDLINE ®, the largest bibliographic database of biomedical citations. Indexers at the US National Library of Medicine (NLM) need efficient tools to he...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of biomedical informatics Vol. 42; no. 5; pp. 814 - 823
Main Authors:	Névéol, Aurélie, Shooshan, Sonya E., Humphrey, Susanne M., Mork, James G., Aronson, Alan R.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.10.2009
Subjects:	Abstracting and Indexing - methods Abstracting and Indexing as Topic/∗methods/statistics & numerical data Artificial Intelligence Dictionaries, Medical Dictionaries, Medical as Topic Evaluation Studies as Topic Humans Medical Subject Headings MEDLINE Natural Language Processing User-Computer Interface MEDLINE Artificial Intelligence Dictionaries, Medical Natural Language Processing Medical Subject Headings Abstracting and Indexing as Topic/∗methods/statistics & numerical data Evaluation Studies as Topic
ISSN:	1532-0464, 1532-0480, 1532-0480
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	The volume of biomedical literature has experienced explosive growth in recent years. This is reflected in the corresponding increase in the size of MEDLINE ®, the largest bibliographic database of biomedical citations. Indexers at the US National Library of Medicine (NLM) need efficient tools to help them accommodate the ensuing workload. After reviewing issues in the automatic assignment of Medical Subject Headings (MeSH ® terms) to biomedical text, we focus more specifically on the new subheading attachment feature for NLM’s Medical Text Indexer (MTI). Natural Language Processing, statistical, and machine learning methods of producing automatic MeSH main heading/subheading pair recommendations were assessed independently and combined. The best combination achieves 48% precision and 30% recall. After validation by NLM indexers, a suitable combination of the methods presented in this paper was integrated into MTI as a subheading attachment feature producing MeSH indexing recommendations compliant with current state-of-the-art indexing practice.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1
ISSN:	1532-0464 1532-0480 1532-0480
DOI:	10.1016/j.jbi.2008.12.007