Big data

Engineering

Health care

Analytics

Pure mathematics

Field (mathematics)

Originality

Economics

Data mining

Computer science

Management science

Qualitative research

Systematic review

Mathematics

Political science

Sociology

Data science

MEDLINE

Economic growth

Knowledge management

Scopus

Publishing

Social science

Management Decision

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.

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