Sensor, Signal, and Imaging Informatics: Big Data and Smart Health Technologies
Identifieur interne : 003214 ( Ncbi/Merge ); précédent : 003213; suivant : 003215Sensor, Signal, and Imaging Informatics: Big Data and Smart Health Technologies
Auteurs : S. Voros [France] ; A. Moreau-Gaudry [France]Source :
- Yearbook of Medical Informatics [ 0943-4747 ] ; 2014.
Abstract
This synopsis presents a selection for the IMIA (International Medical Informatics Association) Yearbook 2014 of excellent research in the broad field of Sensor, Signal, and Imaging Informatics published in the year 2013, with a focus on Big Data and Smart Health Technologies
We performed a systematic initial selection and a double blind peer review process to find the best papers in this domain published in 2013, from the PubMed and Web of Science databases. A set of MeSH keywords provided by experts was used.
Big Data are collections of large and complex datasets which have the potential to capture the whole variability of a study population. More and more innovative sensors are emerging, allowing to enrich these big databases. However they become more and more challenging to process (i.e. capture, store, search, share, transfer, exploit) because traditional tools are not adapted anymore.
This review shows that it is necessary not only to develop new tools specifically designed for Big Data, but also to evaluate their performance on such large datasets.
Url:
DOI: 10.15265/IY-2014-0035
PubMed: 25123735
PubMed Central: 4287096
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Voros</name><affiliation wicri:level="3"><nlm:aff id="aff001"><institution>UJF-Grenoble 1 / CNRS / INSERM, TIMC-IMAG UMR 5525</institution>,<addr-line>Grenoble, F-38041, France</addr-line></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>Grenoble, F-38041</wicri:regionArea><placeName><region type="region" nuts="2">Auvergne-Rhône-Alpes</region><region type="old region" nuts="2">Rhône-Alpes</region></placeName></affiliation></author><author><name sortKey="Moreau Gaudry, A" sort="Moreau Gaudry, A" uniqKey="Moreau Gaudry A" first="A." last="Moreau-Gaudry">A. Moreau-Gaudry</name><affiliation wicri:level="3"><nlm:aff id="aff001"><institution>UJF-Grenoble 1 / CNRS / INSERM, TIMC-IMAG UMR 5525</institution>,<addr-line>Grenoble, F-38041, France</addr-line></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>Grenoble, F-38041</wicri:regionArea><placeName><region type="region" nuts="2">Auvergne-Rhône-Alpes</region><region type="old region" nuts="2">Rhône-Alpes</region></placeName></affiliation><affiliation wicri:level="3"><nlm:aff id="aff002"><institution>UJF-Grenoble 1 / CHU / INSERM CIT803</institution>,<addr-line>Grenoble, F-38041, France</addr-line></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>Grenoble, F-38041</wicri:regionArea><placeName><region type="region" nuts="2">Auvergne-Rhône-Alpes</region><region type="old region" nuts="2">Rhône-Alpes</region></placeName></affiliation></author></analytic><series><title level="j">Yearbook of Medical Informatics</title><idno type="ISSN">0943-4747</idno><idno type="eISSN">2364-0502</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Summary</title><sec><title>Objectives</title><p>This synopsis presents a selection for the IMIA (International Medical Informatics Association) Yearbook 2014 of excellent research in the broad field of Sensor, Signal, and Imaging Informatics published in the year 2013, with a focus on Big Data and Smart Health Technologies</p></sec><sec><title>Methods</title><p>We performed a systematic initial selection and a double blind peer review process to find the best papers in this domain published in 2013, from the PubMed and Web of Science databases. A set of MeSH keywords provided by experts was used.</p></sec><sec><title>Results</title><p>Big Data are collections of large and complex datasets which have the potential to capture the whole variability of a study population. More and more innovative sensors are emerging, allowing to enrich these big databases. However they become more and more challenging to process (i.e. capture, store, search, share, transfer, exploit) because traditional tools are not adapted anymore.</p></sec><sec><title>Conclusions</title><p>This review shows that it is necessary not only to develop new tools specifically designed for Big Data, but also to evaluate their performance on such large datasets.</p></sec></div></front></TEI><pmc article-type="in-brief"><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">Yearb Med Inform</journal-id><journal-id journal-id-type="publisher-id">YMI</journal-id><journal-title-group><journal-title>Yearbook of Medical Informatics</journal-title></journal-title-group><issn pub-type="ppub">0943-4747</issn><issn pub-type="epub">2364-0502</issn><publisher><publisher-name>Schattauer GmbH</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25123735</article-id><article-id pub-id-type="pmc">4287096</article-id><article-id pub-id-type="doi">10.15265/IY-2014-0035</article-id><article-categories><subj-group subj-group-type="heading"><subject>Synopsis</subject></subj-group></article-categories><title-group><article-title>Sensor, Signal, and Imaging Informatics: Big Data and Smart Health Technologies</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Voros</surname><given-names>S.</given-names></name><role>Section Editors for the IMIA Yearbook Section on Sensor, Signal and Imaging Informatics</role><xref ref-type="aff" rid="aff001"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"></xref></contrib><contrib contrib-type="author"><name><surname>Moreau-Gaudry</surname><given-names>A.</given-names></name><role>Section Editors for the IMIA Yearbook Section on Sensor, Signal and Imaging Informatics</role><xref ref-type="aff" rid="aff001"><sup>1</sup></xref><xref ref-type="aff" rid="aff002"><sup>2</sup></xref></contrib></contrib-group><aff id="aff001"><label>1</label><institution>UJF-Grenoble 1 / CNRS / INSERM, TIMC-IMAG UMR 5525</institution>,<addr-line>Grenoble, F-38041, France</addr-line></aff><aff id="aff002"><label>2</label><institution>UJF-Grenoble 1 / CHU / INSERM CIT803</institution>,<addr-line>Grenoble, F-38041, France</addr-line></aff><author-notes><corresp>Correspondence to:</corresp><corresp id="cor1">Sandrine Voros, Laboratoire TIMC-IMAG, équipe GMCAO, IN3S, pavillon Taillefer, Faculté de Médecine, 38706 La Tronche Cedex, France, <phone>+33 4 56 52 00 09</phone>, <fax>+33 4 56 52 00 55</fax>, <email>Sandrine.Voros@imag.fr</email></corresp></author-notes><pub-date pub-type="epub"><day>15</day><month>8</month><year>2014</year></pub-date><pub-date pub-type="collection"><year>2014</year></pub-date><volume>9</volume><issue>1</issue><fpage>150</fpage><lpage>153</lpage><permissions><copyright-statement>© IMIA and Schattauer GmbH 2014</copyright-statement><copyright-year>2014</copyright-year></permissions><abstract><title>Summary</title><sec><title>Objectives</title><p>This synopsis presents a selection for the IMIA (International Medical Informatics Association) Yearbook 2014 of excellent research in the broad field of Sensor, Signal, and Imaging Informatics published in the year 2013, with a focus on Big Data and Smart Health Technologies</p></sec><sec><title>Methods</title><p>We performed a systematic initial selection and a double blind peer review process to find the best papers in this domain published in 2013, from the PubMed and Web of Science databases. A set of MeSH keywords provided by experts was used.</p></sec><sec><title>Results</title><p>Big Data are collections of large and complex datasets which have the potential to capture the whole variability of a study population. More and more innovative sensors are emerging, allowing to enrich these big databases. However they become more and more challenging to process (i.e. capture, store, search, share, transfer, exploit) because traditional tools are not adapted anymore.</p></sec><sec><title>Conclusions</title><p>This review shows that it is necessary not only to develop new tools specifically designed for Big Data, but also to evaluate their performance on such large datasets.</p></sec></abstract><kwd-group><title>Keywords</title><kwd>Big data</kwd><kwd>smart health technologies</kwd></kwd-group><counts><fig-count count="0"></fig-count><table-count count="1"></table-count><equation-count count="0"></equation-count><ref-count count="11"></ref-count><page-count count="4"></page-count></counts></article-meta></front></pmc><affiliations><list><country><li>France</li></country><region><li>Auvergne-Rhône-Alpes</li><li>Rhône-Alpes</li></region></list><tree><country name="France"><region name="Auvergne-Rhône-Alpes"><name sortKey="Voros, S" sort="Voros, S" uniqKey="Voros S" first="S." last="Voros">S. Voros</name></region><name sortKey="Moreau Gaudry, A" sort="Moreau Gaudry, A" uniqKey="Moreau Gaudry A" first="A." last="Moreau-Gaudry">A. Moreau-Gaudry</name><name sortKey="Moreau Gaudry, A" sort="Moreau Gaudry, A" uniqKey="Moreau Gaudry A" first="A." last="Moreau-Gaudry">A. Moreau-Gaudry</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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