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Big Data Analytics in Medicine and Healthcare

This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of comple...

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Detalles Bibliográficos
Autores principales: Ristevski, Blagoj, Chen, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6340124/
https://www.ncbi.nlm.nih.gov/pubmed/29746254
http://dx.doi.org/10.1515/jib-2017-0030
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author Ristevski, Blagoj
Chen, Ming
author_facet Ristevski, Blagoj
Chen, Ming
author_sort Ristevski, Blagoj
collection PubMed
description This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various – omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given.
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spelling pubmed-63401242019-01-28 Big Data Analytics in Medicine and Healthcare Ristevski, Blagoj Chen, Ming J Integr Bioinform Review Article This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various – omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given. De Gruyter 2018-05-10 /pmc/articles/PMC6340124/ /pubmed/29746254 http://dx.doi.org/10.1515/jib-2017-0030 Text en ©2018, Blagoj Ristevski and Ming Chen, published by De Gruyter, Berlin/Boston http://creativecommons.org/licenses/by-nc-nd/4.0 This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
spellingShingle Review Article
Ristevski, Blagoj
Chen, Ming
Big Data Analytics in Medicine and Healthcare
title Big Data Analytics in Medicine and Healthcare
title_full Big Data Analytics in Medicine and Healthcare
title_fullStr Big Data Analytics in Medicine and Healthcare
title_full_unstemmed Big Data Analytics in Medicine and Healthcare
title_short Big Data Analytics in Medicine and Healthcare
title_sort big data analytics in medicine and healthcare
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6340124/
https://www.ncbi.nlm.nih.gov/pubmed/29746254
http://dx.doi.org/10.1515/jib-2017-0030
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