title: A bottom-up approach to MEDLINE indexing recommendations.
abstract: MEDLINE indexing performed by the US National Library of Medicine staff describes the essence of a biomedical publication in about 14 Medical Subject Headings (MeSH). Since 2002, this task is assisted by the Medical Text Indexer (MTI) program. We present a bottom-up approach to MEDLINE indexing in which the abstract is searched for indicators for a specific MeSH recommendation in a two-step process. Supervised machine learning combined with triage rules improves sensitivity of recommendations while keeping the number of recommended terms relatively small. Improvement in recommendations observed in this work warrants further exploration of this approach to MTI recommendations on a larger set of MeSH headings.
authors.affiliation: National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.
authors.forename: Antonio
authors.initials: A
authors.lastname: Jimeno-Yepes
authors.forename: Bartłomiej
authors.initials: B
authors.lastname: Wilkowski
authors.forename: James G
authors.initials: JG
authors.lastname: Mork
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Van Lenten
authors.forename: Dina Demner
authors.initials: DD
authors.lastname: Fushman
authors.forename: Alan R
authors.initials: AR
authors.lastname: Aronson
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D002240:Carbohydrate Sequence; D016239:MEDLINE; D046650:Medical Subject Headings; D009323:Natural Language Processing; D017432:Unified Medical Language System