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Big Data Application in Biomedical Research and Health Care: A Literature Review

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of b...

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Detalles Bibliográficos
Autores principales: Luo, Jake, Wu, Min, Gopukumar, Deepika, Zhao, Yiqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Libertas Academica 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720168/
https://www.ncbi.nlm.nih.gov/pubmed/26843812
http://dx.doi.org/10.4137/BII.S31559
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author Luo, Jake
Wu, Min
Gopukumar, Deepika
Zhao, Yiqing
author_facet Luo, Jake
Wu, Min
Gopukumar, Deepika
Zhao, Yiqing
author_sort Luo, Jake
collection PubMed
description Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.
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spelling pubmed-47201682016-02-03 Big Data Application in Biomedical Research and Health Care: A Literature Review Luo, Jake Wu, Min Gopukumar, Deepika Zhao, Yiqing Biomed Inform Insights Review Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care. Libertas Academica 2016-01-19 /pmc/articles/PMC4720168/ /pubmed/26843812 http://dx.doi.org/10.4137/BII.S31559 Text en © 2016 the author(s), publisher and licensee Libertas Academica Ltd. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.
spellingShingle Review
Luo, Jake
Wu, Min
Gopukumar, Deepika
Zhao, Yiqing
Big Data Application in Biomedical Research and Health Care: A Literature Review
title Big Data Application in Biomedical Research and Health Care: A Literature Review
title_full Big Data Application in Biomedical Research and Health Care: A Literature Review
title_fullStr Big Data Application in Biomedical Research and Health Care: A Literature Review
title_full_unstemmed Big Data Application in Biomedical Research and Health Care: A Literature Review
title_short Big Data Application in Biomedical Research and Health Care: A Literature Review
title_sort big data application in biomedical research and health care: a literature review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720168/
https://www.ncbi.nlm.nih.gov/pubmed/26843812
http://dx.doi.org/10.4137/BII.S31559
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