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1. Huang M, Névéol A, Lu Z: Recommending MeSH terms for annotating biomedical articles. J Am Med Inform Assoc; 2011 Sep-Oct;18(5):660-7
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Recommending MeSH terms for annotating biomedical articles.
  • BACKGROUND: Due to the high cost of manual curation of key aspects from the scientific literature, automated methods for assisting this process are greatly desired.
  • Here, we report a novel approach to facilitate MeSH indexing, a challenging task of assigning MeSH terms to MEDLINE citations for their archiving and retrieval.
  • METHODS: Unlike previous methods for automatic MeSH term assignment, we reformulate the indexing task as a ranking problem such that relevant MeSH headings are ranked higher than those irrelevant ones.
  • Specifically, for each document we retrieve 20 neighbor documents, obtain a list of MeSH main headings from neighbors, and rank the MeSH main headings using ListNet-a learning-to-rank algorithm.
  • We trained our algorithm on 200 documents and tested on a previously used benchmark set of 200 documents and a larger dataset of 1000 documents.
  • RESULTS: Tested on the benchmark dataset, our method achieved a precision of 0.390, recall of 0.712, and mean average precision (MAP) of 0.626.
  • In comparison to the state of the art, we observe statistically significant improvements as large as 39% in MAP (p-value <0.001).
  • Similar significant improvements were also obtained on the larger document set.
  • CONCLUSION: Experimental results show that our approach makes the most accurate MeSH predictions to date, which suggests its great potential in making a practical impact on MeSH indexing.
  • Furthermore, as discussed the proposed learning framework is robust and can be adapted to many other similar tasks beyond MeSH indexing in the biomedical domain.
  • All data sets are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/indexing.
  • [MeSH-major] Abstracting and Indexing as Topic / methods. Medical Subject Headings. Natural Language Processing. PubMed
  • [MeSH-minor] Algorithms. Artificial Intelligence. Automation. Humans. Semantics. United States

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  • (PMID = 21613640.001).
  • [ISSN] 1527-974X
  • [Journal-full-title] Journal of the American Medical Informatics Association : JAMIA
  • [ISO-abbreviation] J Am Med Inform Assoc
  • [Language] eng
  • [Grant] United States / Intramural NIH HHS / / ZIA LM091711-01
  • [Publication-type] Evaluation Studies; Journal Article; Research Support, N.I.H., Intramural; Research Support, Non-U.S. Gov't
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC3168302
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