Comparison and combination of several MeSH indexing approaches

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 pr...

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Vydáno v:AMIA ... Annual Symposium proceedings Ročník 2013; s. 709
Hlavní autoři: Yepes, Antonio Jose Jimeno, Mork, James G, Demner-Fushman, Dina, Aronson, Alan R
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 2013
Témata:
Abstracting and Indexing as Topic - methods
Algorithms
Artificial Intelligence
Medical Subject Headings
MEDLINE
Natural Language Processing
ISSN:1942-597X, 1559-4076
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Shrnutí: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.
Bibliografie:ObjectType-Article-2
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ISSN:1942-597X
1559-4076