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Sensor, Signal, and Imaging Informatics: Big Data and Smart Health Technologies

Identifieur interne : 003214 ( Ncbi/Checkpoint ); précédent : 003213; suivant : 003215

Sensor, 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.

RBID : PMC:4287096

Abstract

SummaryObjectives

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

Methods

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.

Results

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.

Conclusions

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:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287096

DOI: 10.15265/IY-2014-0035
PubMed: 25123735
PubMed Central: 4287096

Affiliations:

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PMC:4287096

Le document en format XML

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<front><div type="abstract" xml:lang="en"><title>Summary</title>
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<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>
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Sensor, Signal, and Imaging Informatics: Big Data and Smart Health Technologies

Sensor, Signal, and Imaging Informatics: Big Data and Smart Health Technologies

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Abstract

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Le document en format XML

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