title: Comparison and combination of several MeSH indexing approaches.
abstract: MeSH indexing of MEDLINE is becoming a more difficult task for the group of highly qualified indexing staff at the US National Library of Medicine, due to the large yearly growth of MEDLINE and the increasing size of MeSH. Since 2002, this task has been assisted by the Medical Text Indexer or MTI program. We extend previous machine learning analysis by adding a more diverse set of MeSH headings targeting examples where MTI has been shown to perform poorly. Machine learning algorithms exceed MTI's performance on MeSH headings that are used very frequently and headings for which the indexing frequency is very low. We find that when we combine the MTI suggestions and the prediction of the learning algorithms, the performance improves compared to any single method for most of the evaluated MeSH headings.
authors.affiliation: NICTA Victoria Research Lab, Melbourne VIC 3010, Australia; ; National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.
authors.forename: Antonio Jose Jimeno
authors.initials: AJ
authors.lastname: Yepes
authors.affiliation: National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.
authors.forename: James G
authors.initials: JG
authors.lastname: Mork
authors.affiliation: National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.
authors.forename: Dina
authors.initials: D
authors.lastname: Demner-Fushman
authors.affiliation: National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.
authors.forename: Alan R
authors.initials: AR
authors.lastname: Aronson
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D016239:MEDLINE; D046650:Medical Subject Headings; D009323:Natural Language Processing