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dc.contributor.authorSalazar-Reyna, Roberto
dc.contributor.authorGonzalez-Aleu, Fernando
dc.contributor.authorGranda-Gutierrez, Edgar M.A.
dc.contributor.authorDiaz-Ramirez, Jenny
dc.contributor.authorGarza-Reyes, Jose Arturo
dc.contributor.authorKumar, Anil
dc.date.accessioned2020-12-15T14:49:40Z
dc.date.available2020-12-15T14:49:40Z
dc.date.issued2020-12-07
dc.identifier.citationSalazar-Reyna, R., Gonzalez-Aleu, F., Granda-Gutierrez, E.M., Diaz-Ramirez, J., Garza-Reyes, J.A. and Kumar, A., 2020. A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems. Management Decision, pp. 1-20.en_US
dc.identifier.doi10.1108/MD-01-2020-0035
dc.identifier.urihttp://hdl.handle.net/10545/625471
dc.description.abstractThe objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining, and machine learning to healthcare engineering systems. A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest, and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors, and content. From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field. The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors’ previous knowledge and the nature of the publications were used to select different platforms. To the best of the authors’ knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining, and machine learning applied to healthcare engineering systems.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherEmeralden_US
dc.relation.urlhttps://www.emerald.com/insight/content/doi/10.1108/MD-01-2020-0035/full/htmlen_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectData analytics, big data, machine learning, healthcare systems, systematic literature reviewen_US
dc.titleA systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systemsen_US
dc.typeArticleen_US
dc.identifier.eissn1758-6070
dc.contributor.departmentUniversidad de Monterrey, San Pedro Garza Garcia, Mexicoen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentLondon Metropolitan Universityen_US
dc.identifier.journalManagement Decisionen_US
dcterms.dateAccepted2020-05-14
refterms.dateFOA2020-12-15T14:49:41Z
dc.author.detail780891en_US


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