Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine
Identifieur interne : 000010 ( Ncbi/Merge ); précédent : 000009; suivant : 000011Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine
Auteurs : Y. Aphinyanaphongs ; C. F. AliferisSource :
- AMIA Annual Symposium Proceedings [ 1942-597X ] ; 2003.
Abstract
The discipline of Evidence Based Medicine (EBM) studies formal and quasi-formal methods for identifying high quality medical information and abstracting it in useful forms so that patients receive the best customized care possible [
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PubMed: 14728128
PubMed Central: 1480096
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine</title><author><name sortKey="Aphinyanaphongs, Y" sort="Aphinyanaphongs, Y" uniqKey="Aphinyanaphongs Y" first="Y." last="Aphinyanaphongs">Y. Aphinyanaphongs</name></author><author><name sortKey="Aliferis, C F" sort="Aliferis, C F" uniqKey="Aliferis C" first="C. F." last="Aliferis">C. F. Aliferis</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">14728128</idno><idno type="pmc">1480096</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1480096</idno><idno type="RBID">PMC:1480096</idno><date when="2003">2003</date><idno type="wicri:Area/Pmc/Corpus">000111</idno><idno type="wicri:Area/Pmc/Curation">000111</idno><idno type="wicri:Area/Pmc/Checkpoint">000190</idno><idno type="wicri:Area/Ncbi/Merge">000010</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine</title><author><name sortKey="Aphinyanaphongs, Y" sort="Aphinyanaphongs, Y" uniqKey="Aphinyanaphongs Y" first="Y." last="Aphinyanaphongs">Y. Aphinyanaphongs</name></author><author><name sortKey="Aliferis, C F" sort="Aliferis, C F" uniqKey="Aliferis C" first="C. F." last="Aliferis">C. F. Aliferis</name></author></analytic><series><title level="j">AMIA Annual Symposium Proceedings</title><idno type="eISSN">1942-597X</idno><imprint><date when="2003">2003</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The discipline of Evidence Based Medicine (EBM) studies formal and quasi-formal methods for identifying high quality medical information and abstracting it in useful forms so that patients receive the best customized care possible [<xref ref-type="bibr" rid="b1-007">1</xref>]. Current computer-based methods for finding high quality information in PubMed and similar bibliographic resources utilize search tools that employ preconstructed Boolean queries. These clinical queries are derived from a combined application of (a) user interviews, (b) ad-hoc manual document quality review, and (c) search over a constrained space of disjunctive Boolean queries. The present research explores the use of powerful text categorization (machine learning) methods to identify content-specific and high-quality PubMed articles. Our results show that models built with the proposed approach outperform the Boolean based PubMed clinical query filters in discriminatory power.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">AMIA Annu Symp Proc</journal-id><journal-title-group><journal-title>AMIA Annual Symposium Proceedings</journal-title></journal-title-group><issn pub-type="epub">1942-597X</issn><publisher><publisher-name>American Medical Informatics Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">14728128</article-id><article-id pub-id-type="pmc">1480096</article-id><article-id pub-id-type="publisher-id">007</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Aphinyanaphongs</surname><given-names>Y.</given-names></name><degrees>M.S.</degrees></contrib><contrib contrib-type="author"><name><surname>Aliferis</surname><given-names>C.F.</given-names></name><degrees>M.D., Ph.D.</degrees></contrib><aff id="af1-007">Department of Biomedical Informatics, Vanderbilt University, Nashville, TN</aff></contrib-group><pub-date pub-type="ppub"><year>2003</year></pub-date><volume>2003</volume><fpage>31</fpage><lpage>35</lpage><permissions><copyright-statement>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose.</copyright-statement></permissions><abstract><p>The discipline of Evidence Based Medicine (EBM) studies formal and quasi-formal methods for identifying high quality medical information and abstracting it in useful forms so that patients receive the best customized care possible [<xref ref-type="bibr" rid="b1-007">1</xref>]. Current computer-based methods for finding high quality information in PubMed and similar bibliographic resources utilize search tools that employ preconstructed Boolean queries. These clinical queries are derived from a combined application of (a) user interviews, (b) ad-hoc manual document quality review, and (c) search over a constrained space of disjunctive Boolean queries. The present research explores the use of powerful text categorization (machine learning) methods to identify content-specific and high-quality PubMed articles. Our results show that models built with the proposed approach outperform the Boolean based PubMed clinical query filters in discriminatory power.</p></abstract></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Aliferis, C F" sort="Aliferis, C F" uniqKey="Aliferis C" first="C. F." last="Aliferis">C. F. Aliferis</name><name sortKey="Aphinyanaphongs, Y" sort="Aphinyanaphongs, Y" uniqKey="Aphinyanaphongs Y" first="Y." last="Aphinyanaphongs">Y. Aphinyanaphongs</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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