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...
| Publicado en: | Biomedical Informatics Insights no. 8; pp. 1 - 11 |
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| Autores principales: | , , , |
| Formato: | research systematic review Journal Article |
| Publicado: |
Sage Publications Inc.
2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=118546796&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118546796 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11782226 B077 jtl: Biomedical Informatics Insights issn: 11782226 maglogo: Y pubinfo: dt: 2016 iid: 8 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 118546796 118546796 118546796 10.4137/BII.s31559 118546796 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Big Data Application in Biomedical Research and Health Care: A Literature Review. aug: au: Luo, Jake Min Wu Gopukumar, Deepika Yiqing Zhao affil: College of Health Science, Department of Health Informatics and Administration, Centerfor Biomedical Data and Language Processing, University of Wisconsin-Milwaukee, Milwaukee, WI, USA sug: subj: Bioinformatics Sequence Analysis Data Analytics Informatics Research Decision Making Health Literature Review Data Analysis Systematic Review Data Mining Clinical Trials Logistic Regression Time Series Prospective Studies Case Studies Data Collection Surveys Scales ab: 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. pubtype: Academic Journal doctype: research systematic review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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