Uncovering text mining: A survey of current work on web-based epidemic intelligence
Identifieur interne : 001033 ( Pmc/Curation ); précédent : 001032; suivant : 001034Uncovering text mining: A survey of current work on web-based epidemic intelligence
Auteurs : Nigel Collier [Japon]Source :
- Global Public Health [ 1744-1692 ] ; 2012.
Abstract
Real world pandemics such as SARS 2002 as well as popular fiction like the movie Contagion graphically depict the health threat of a global pandemic and the key role of epidemic intelligence (EI). While EI relies heavily on established indicator sources a new class of methods based on event alerting from unstructured digital Internet media is rapidly becoming acknowledged within the public health community. At the heart of automated information gathering systems is a technology called
Url:
DOI: 10.1080/17441692.2012.699975
PubMed: 22783909
PubMed Central: 3438486
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Uncovering text mining: A survey of current work on web-based epidemic intelligence</title><author><name sortKey="Collier, Nigel" sort="Collier, Nigel" uniqKey="Collier N" first="Nigel" last="Collier">Nigel Collier</name><affiliation wicri:level="1"><nlm:aff id="A1">National Institute of Informatics, Tokyo, Japan</nlm:aff><country xml:lang="fr">Japon</country><wicri:regionArea>National Institute of Informatics, Tokyo</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22783909</idno><idno type="pmc">3438486</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3438486</idno><idno type="RBID">PMC:3438486</idno><idno type="doi">10.1080/17441692.2012.699975</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">001033</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001033</idno><idno type="wicri:Area/Pmc/Curation">001033</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">001033</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Uncovering text mining: A survey of current work on web-based epidemic intelligence</title><author><name sortKey="Collier, Nigel" sort="Collier, Nigel" uniqKey="Collier N" first="Nigel" last="Collier">Nigel Collier</name><affiliation wicri:level="1"><nlm:aff id="A1">National Institute of Informatics, Tokyo, Japan</nlm:aff><country xml:lang="fr">Japon</country><wicri:regionArea>National Institute of Informatics, Tokyo</wicri:regionArea></affiliation></author></analytic><series><title level="j">Global Public Health</title><idno type="ISSN">1744-1692</idno><idno type="eISSN">1744-1706</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Real world pandemics such as SARS 2002 as well as popular fiction like the movie Contagion graphically depict the health threat of a global pandemic and the key role of epidemic intelligence (EI). While EI relies heavily on established indicator sources a new class of methods based on event alerting from unstructured digital Internet media is rapidly becoming acknowledged within the public health community. At the heart of automated information gathering systems is a technology called <italic>text mining.</italic> My contribution here is to provide an overview of the role that text mining technology plays in detecting epidemics and to synthesise my existing research on the BioCaster project.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Berry, M W" uniqKey="Berry M">M.W Berry</name></author><author><name sortKey="Kogan, M" uniqKey="Kogan M">M. Kogan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Brownstein, J" uniqKey="Brownstein J">J. Brownstein</name></author><author><name sortKey="Freifeld, C" uniqKey="Freifeld C">C. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Glob Public Health</journal-id><journal-id journal-id-type="iso-abbrev">Glob Public Health</journal-id><journal-id journal-id-type="publisher-id">rgph</journal-id><journal-title-group><journal-title>Global Public Health</journal-title></journal-title-group><issn pub-type="ppub">1744-1692</issn><issn pub-type="epub">1744-1706</issn><publisher><publisher-name>Taylor & Francis</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22783909</article-id><article-id pub-id-type="pmc">3438486</article-id><article-id pub-id-type="doi">10.1080/17441692.2012.699975</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>Uncovering text mining: A survey of current work on web-based epidemic intelligence</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Collier</surname><given-names>Nigel</given-names></name><xref ref-type="aff" rid="A1"></xref><xref ref-type="corresp" rid="COR1">*</xref></contrib><aff id="A1">National Institute of Informatics, Tokyo, Japan</aff></contrib-group><author-notes><corresp id="COR1"><label>*</label>Email: <email>collier@nii.ac.jp</email></corresp></author-notes><pub-date pub-type="epub"><day>11</day><month>7</month><year>2012</year></pub-date><pub-date pub-type="ppub"><month>8</month><year>2012</year></pub-date><volume>7</volume><issue>7</issue><fpage>731</fpage><lpage>749</lpage><history><date date-type="received"><day>20</day><month>10</month><year>2011</year></date><date date-type="rev-recd"><day>6</day><month>3</month><year>2012</year></date></history><permissions><copyright-statement>© 2012 Taylor & Francis</copyright-statement><copyright-year>2012</copyright-year><license license-type="open-access" xlink:href="http://www.informaworld.com/mpp/uploads/iopenaccess_tcs.pdf"><license-p>This is an open access article distributed under the <ext-link ext-link-type="uri" xlink:href="http://www.informaworld.com/mpp/uploads/iopenaccess_tcs.pdf">Supplemental Terms and Conditions for iOpenAccess articles published in Taylor & Francis journals</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract><p>Real world pandemics such as SARS 2002 as well as popular fiction like the movie Contagion graphically depict the health threat of a global pandemic and the key role of epidemic intelligence (EI). While EI relies heavily on established indicator sources a new class of methods based on event alerting from unstructured digital Internet media is rapidly becoming acknowledged within the public health community. At the heart of automated information gathering systems is a technology called <italic>text mining.</italic> My contribution here is to provide an overview of the role that text mining technology plays in detecting epidemics and to synthesise my existing research on the BioCaster project.</p></abstract><kwd-group><kwd>natural language processing</kwd><kwd>BioCaster</kwd><kwd>text mining</kwd><kwd>artificial intelligence</kwd><kwd>ontologies</kwd><kwd>evaluation</kwd><kwd>web-based discovery</kwd><kwd>social media</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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