A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems. Issue 2 (7th December 2020)

• Record Type: Journal Article
• Authors: Salazar-Reyna, Roberto; Gonzalez-Aleu, Fernando; Granda-Gutierrez, Edgar M.A.; Diaz-Ramirez, Jenny; Garza-Reyes, Jose Arturo; Kumar, Anil
• Is Part Of: Management decision. Volume 60:Issue 2(2022)
• Page Start: 300
• Page End: 319
• Publication Date: 2020-12-07
• Subjects: Data analytics -- Big data -- Machine learning -- Healthcare systems -- Systematic literature review; Management -- Periodicals; 658.403
• DOI: 10.1108/MD-01-2020-0035
• Languages: English
• ISSNs: 0025-1747

Abstract

Abstract : Purpose: The 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/approach: 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. Findings: 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. Research limitations/implications: 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. Originality/value: 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.