Linked Data and Online Classifications to Organise Mined Patterns in Patient Data
Identifieur interne : 000054 ( Pmc/Curation ); précédent : 000053; suivant : 000055Linked Data and Online Classifications to Organise Mined Patterns in Patient Data
Auteurs : Nicolas Jay [France] ; Mathieu D Quin [Royaume-Uni]Source :
- AMIA Annual Symposium Proceedings [ 1942-597X ] ; 2013.
Abstract
In this paper, we investigate the use of web data resources in medicine, especially through medical classifications made available using the principles of Linked Data, to support the interpretation of patterns mined from patient care trajectories. Interpreting such patterns is naturally a challenge for an analyst, as it requires going through large amounts of results and access to sufficient background knowledge. We employ linked data, especially as exposed through the BioPortal system, to create a navigation structure within the patterns obtained form sequential pattern mining. We show how this approach provides a flexible way to explore data about trajectories of diagnoses and treatments according to different medical classifications.
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PubMed: 24551369
PubMed Central: 3900210
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<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">24551369</article-id><article-id pub-id-type="pmc">3900210</article-id><article-id pub-id-type="publisher-id">amia_2013_symposium_681</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group></article-categories><title-group><article-title>Linked Data and Online Classifications to Organise Mined Patterns in Patient Data</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Jay</surname><given-names>Nicolas</given-names></name><degrees>MD, PhD</degrees><xref ref-type="aff" rid="af1-amia_2013_symposium_681"><sup>1</sup></xref><xref ref-type="aff" rid="af2-amia_2013_symposium_681"><sup>2</sup></xref><xref ref-type="corresp" rid="c1-amia_2013_symposium_681"></xref></contrib><contrib contrib-type="author"><name><surname>d’Aquin</surname><given-names>Mathieu</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="af3-amia_2013_symposium_681"><sup>3</sup></xref><xref ref-type="corresp" rid="c2-amia_2013_symposium_681"></xref></contrib></contrib-group><aff id="af1-amia_2013_symposium_681"><label>1</label>Université de Lorraine, LORIA, UMR 7503Vandoevre-lès-Nancy, F-54506, France</aff><aff id="af2-amia_2013_symposium_681"><label>2</label>CHU de Nancy Nancy, F-54000, France</aff><aff id="af3-amia_2013_symposium_681"><label>3</label>Knowledge Media Institute, The Open UniversityWalton Hall, Milton Keynes, MK7 6AA, UK</aff><author-notes><corresp id="c1-amia_2013_symposium_681"><email>nicolas.jay@loria.fr</email></corresp><corresp id="c2-amia_2013_symposium_681"><email>mathieu.daquin@open.ac.uk</email></corresp></author-notes><pub-date pub-type="collection"><year>2013</year></pub-date><pub-date pub-type="epub"><day>16</day><month>11</month><year>2013</year></pub-date><volume>2013</volume><fpage>681</fpage><lpage>690</lpage><permissions><copyright-statement>©2013 AMIA - All rights reserved.</copyright-statement><copyright-year>2013</copyright-year><license><license-p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose</license-p></license></permissions><abstract><p>In this paper, we investigate the use of web data resources in medicine, especially through medical classifications made available using the principles of Linked Data, to support the interpretation of patterns mined from patient care trajectories. Interpreting such patterns is naturally a challenge for an analyst, as it requires going through large amounts of results and access to sufficient background knowledge. We employ linked data, especially as exposed through the BioPortal system, to create a navigation structure within the patterns obtained form sequential pattern mining. We show how this approach provides a flexible way to explore data about trajectories of diagnoses and treatments according to different medical classifications.</p></abstract></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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