A High Recall Classifier for Selecting Articles for MEDLINE Indexing.
Journal
AMIA ... Annual Symposium proceedings. AMIA Symposium
ISSN: 1942-597X
Titre abrégé: AMIA Annu Symp Proc
Pays: United States
ID NLM: 101209213
Informations de publication
Date de publication:
2019
2019
Historique:
entrez:
21
4
2020
pubmed:
21
4
2020
medline:
22
8
2020
Statut:
epublish
Résumé
MEDLINE is the National Library of Medicine's premier bibliographic database for biomedical literature. A highly valuable feature of the database is that each record is manually indexed with a controlled vocabulary called MeSH. Most MEDLINE journals are indexed cover-to-cover, but there are about 200 selectively indexed journals for which only articles related to biomedicine and life sciences are indexed. In recent years, the selection process has become an increasing burden for indexing staff, and this paper presents a machine learning based system that offers very significant time savings by semi-automating the task. At the core of the system is a high recall classifier for the identification of journal articles that are in-scope for MEDLINE. The system is shown to reduce the number of articles requiring manual review by 54%, equivalent to approximately 40,000 articles per year.
Types de publication
Journal Article
Research Support, N.I.H., Intramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
727-734Informations de copyright
©2019 AMIA - All rights reserved.
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