A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

PurposeThe 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.Design/methodology/approachA systematic literature review (SLR) was conducted to obtain t...

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Bibliographic Details
Published in:Management decision Vol. 60; no. 2; pp. 300 - 319
Main Authors: Salazar-Reyna, Roberto, Gonzalez-Aleu, Fernando, Granda-Gutierrez, Edgar M.A, Diaz-Ramirez, Jenny, Garza-Reyes, Jose Arturo, Kumar, Anil
Format: Journal Article
Language:English
Published: London Emerald Publishing Limited 02.02.2022
Emerald Group Publishing Limited
Subjects:
Algorithms
Bibliometrics
Big Data
Data analysis
Data mining
Data science
Engineering
Health care industry
Informatics
Literature reviews
Machine learning
Research methodology
Scientometrics
Social network analysis
Social networks
Systematic review
Trends
Data analytics
Big data
Healthcare systems
Machine learning
Systematic literature review
ISSN:0025-1747, 1758-6070
Online Access:Get full text
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Summary:PurposeThe 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.Design/methodology/approachA 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.FindingsFrom 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.Research limitations/implicationsThe 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.Originality/valueTo 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.
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ISSN:0025-1747
1758-6070
DOI:10.1108/MD-01-2020-0035