Big Data Analytics in Medicine and Healthcare

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Abstract

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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Big data analytics in healthcare: promise and potential

Wullianallur Raghupathi, Viju Raghupathi (2014)

Objective To describe the promise and potential of big data analytics in healthcare. Methods The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Results The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Conclusions Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.

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Big data for health.

Javier Andreu-Perez, Carmen C Y Poon, Robert Merrifield … (2015)

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

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

Jake Luo, Min Wu, Deepika Gopukumar … (2016)

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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Blagoj Ristevski:

ORCID: http://orcid.org/0000-0002-8356-1203

Journal

Journal ID (nlm-ta): J Integr Bioinform

Journal ID (iso-abbrev): J Integr Bioinform

Journal ID (publisher-id): jib

Journal ID (doi): jib

Journal ID (pmc): jib

Title: Journal of Integrative Bioinformatics

Publisher: De Gruyter

ISSN (Electronic): 1613-4516

Publication date (Electronic): 10 May 2018

Publication date Collection: September 2018

Volume: 15

Issue: 3

Electronic Location Identifier: 20170030

Affiliations

“St. Kliment Ohridski” University – Bitola, Faculty of Information and Communication Technologies , ul. Partizanska bb, 7000 Bitola, Republic of Macedonia

deptDepartment of Bioinformatics , College of Life Sciences, Zhejiang University Zijingang Campus , Hangzhou, P.R. China

Author information

Blagoj Ristevski http://orcid.org/0000-0002-8356-1203

Article

Publisher ID: jib-2017-0030

DOI: 10.1515/jib-2017-0030

PMC ID: 6340124

PubMed ID: 29746254

SO-VID: ed374493-2a81-42d8-8947-70ef0979b198

License:

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

History

Date received : 07 April 2017

Date revision received : 16 January 2018

Date accepted : 20 March 2018

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Figures: 1, Tables: 1, References: 25, Pages: 5

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Most cited references 19

Big data analytics in healthcare: promise and potential

Big data for health.

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

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