Format

Send to

Choose Destination
J Med Syst. 2017 Oct 14;41(11):183. doi: 10.1007/s10916-017-0832-2.

A Systematic Review of Techniques and Sources of Big Data in the Healthcare Sector.

Author information

1
Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain.
2
Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain. isator@tel.uva.es.
3
National Institute of Telecommunications (Inatel), Santa Rita do Sapucaí, MG, Brazil.
4
Instituto de Telecomunicações, Covilhã, Portugal.
5
ITMO University, St. Petersburg, Russia.
6
University of Fortaleza (UNIFOR), Fortaleza, CE, Brazil.
7
Bretagne Loire and Nantes Universities, UMR 6164, IETR Polytech, Nantes, France.

Abstract

The main objective of this paper is to present a review of existing researches in the literature, referring to Big Data sources and techniques in health sector and to identify which of these techniques are the most used in the prediction of chronic diseases. Academic databases and systems such as IEEE Xplore, Scopus, PubMed and Science Direct were searched, considering the date of publication from 2006 until the present time. Several search criteria were established as 'techniques' OR 'sources' AND 'Big Data' AND 'medicine' OR 'health', 'techniques' AND 'Big Data' AND 'chronic diseases', etc. Selecting the paper considered of interest regarding the description of the techniques and sources of Big Data in healthcare. It found a total of 110 articles on techniques and sources of Big Data on health from which only 32 have been identified as relevant work. Many of the articles show the platforms of Big Data, sources, databases used and identify the techniques most used in the prediction of chronic diseases. From the review of the analyzed research articles, it can be noticed that the sources and techniques of Big Data used in the health sector represent a relevant factor in terms of effectiveness, since it allows the application of predictive analysis techniques in tasks such as: identification of patients at risk of reentry or prevention of hospital or chronic diseases infections, obtaining predictive models of quality.

KEYWORDS:

Big data; Chronic diseases; Data mining; Health sector; Sources; Techniques

PMID:
29032458
DOI:
10.1007/s10916-017-0832-2
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center