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1. Siadaty MS, Shu J, Knaus WA: Relemed: sentence-level search engine with relevance score for the MEDLINE database of biomedical articles. BMC Med Inform Decis Mak; 2007;7:1
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Relemed: sentence-level search engine with relevance score for the MEDLINE database of biomedical articles.
  • BACKGROUND: Receiving extraneous articles in response to a query submitted to MEDLINE/PubMed is common.
  • When submitting a multi-word query (which is the majority of queries submitted), the presence of all query words within each article may be a necessary condition for retrieving relevant articles, but not sufficient.
  • Ideally a relationship between the query words in the article is also required.
  • We propose that if two words occur within an article, the probability that a relation between them is explained is higher when the words occur within adjacent sentences versus remote sentences.
  • Therefore, sentence-level concurrence can be used as a surrogate for existence of the relationship between the words.
  • In order to avoid the irrelevant articles, one solution would be to increase the search specificity.
  • Another solution is to estimate a relevance score to sort the retrieved articles.
  • However among the >30 retrieval services available for MEDLINE, only a few estimate a relevance score, and none detects and incorporates the relation between the query words as part of the relevance score.
  • RESULTS: We have developed "Relemed", a search engine for MEDLINE.
  • Relemed increases specificity and precision of retrieval by searching for query words within sentences rather than the whole article.
  • It uses sentence-level concurrence as a statistical surrogate for the existence of relationship between the words.
  • It also estimates a relevance score and sorts the results on this basis, thus shifting irrelevant articles lower down the list.
  • In two case studies, we demonstrate that the most relevant articles appear at the top of the Relemed results, while this is not necessarily the case with a PubMed search.
  • We have also shown that a Relemed search includes not only all the articles retrieved by PubMed, but potentially additional relevant articles, due to the extended 'automatic term mapping' and text-word searching features implemented in Relemed.
  • CONCLUSION: By using sentence-level matching, Relemed can deliver higher specificity, thus eliminating more false-positive articles.
  • By introducing an appropriate relevance metric, the most relevant articles on which the user wishes to focus are listed first.
  • Relemed also shrinks the displayed text, and hence the time spent scanning the articles.
  • [MeSH-major] Information Storage and Retrieval / methods. MEDLINE / standards
  • [MeSH-minor] Algorithms

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  • [Cites] Psychol Rev. 1954 Nov;61(6):401-9 [13215690.001]
  • [Cites] Pediatr Pathol. 1991 Sep-Oct;11(5):677-84 [1745639.001]
  • (PMID = 17214888.001).
  • [ISSN] 1472-6947
  • [Journal-full-title] BMC medical informatics and decision making
  • [ISO-abbreviation] BMC Med Inform Decis Mak
  • [Language] eng
  • [Publication-type] Journal Article
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC1780044
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