Machine learning in occupational safety and health: protocol for a systematic review

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Maheronnaghsh, S (Author) Other; Zolfagharnasab, H (Author) Other; Gorgich, M (Author) Other; Duarte, J (Author) FEUP

Journal

Title: International Journal of Occupational and Environmental Safety

Vol. 19

Pages: 32-38

ISSN: 2184-0954

Publisher: FEUP - Sociedade Portuguesa de Segurança e Higiene Ocupacionais

Other information

Authenticus ID: P-00X-CN9

DOI: 10.24840/2184-0954_005.001_0004

Abstract (EN): <jats:p>Industry 4.0 has shaped the way people look at the world and interact with it, especially concerning the work environment with respect to occupational safety and health (OSH). Machine learning (ML), as a branch of Artificial Intelligence (AI), can be effectively used to create expert systems to exhibit intelligent behavior to provide solutions to complicated problems and finally process massive data. Therefore, a study is proposed to provide the best methodological practice in the light of ML. Alongside the review of previous investigations, the following research aims to determine the ML approaches appropriate to OSH issues. In other words, highlighting specific ML methodologies, which have been employed successfully in others areas. Bearing this objective in mind, one can identify an appropriate ML technique to solve a problem in the OSH domain. Accordingly, several questions were designed to conduct the research. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Protocols and Systematic Reviews were used to draw the research outline. The chosen databases were SCOPUS, PubMed, Science Direct, Inspect, and Web of Science. A set of keywords related to the topic were defined, and both exclusion and inclusion criteria were determined. All of the eligible papers will be analyzed, and the extracted information will be included in an Excel form sheet. The results will be presented in a narrative-based form. Additionally, all tables summarizing the most important findings will be offered.</jats:p>

Language: English

Type (Professor's evaluation): Scientific