Systematic Review

Data mining in occupational safety and health: a systematic mapping and roadmap

Beatriz Lavezo dos Reis; Ana Caroline Francisco da Rosa; Ageu de Araujo Machado; Simone Luzia Santana Sambugaro Wencel; Gislaine Camila Lapasini Leal; Edwin Vladimir Cardoza Galdamez; Rodrigo Clemente Thom de Souza

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Paper aims: This research presents a literature overview in relation to data mining and machine learning applications in the area of occupational health and safety.

Originality: A summary of main insights obtained from the analysis of systematic mapping is presented at the end, as well as a roadmap with recommendations for directing future research on the topic.

Research method: This article carries out a thorough descriptive research of the scientific literature on the topic through a systematic mapping covering the period between the years 2008 and 2019 and 12 scientific databases, which at the end presents 68 selected records.

Main findings: Around 84% of the selected records were of total significance for the research, with the majority of them being classified in the areas of civil construction and steel industry.

Implications for theory and practice: Through this study it is possible to understand the way research has been developed on this theme, as well as point to the guidelines for future studies. Other contribution is the indication of studies in OSH 4.0 concept, based on monitoring workers full-time.


Machine learning, Safety and health at work, Occupational accidents


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