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.
BACKGROUND: Occupational safety risk management is a systemic process capable of promoting technical engineering solutions, considering a wide range of predictable, unexpected and subjective factors related to accident occurrences. In Brazil, the behavior of managers in relation to risk management tends to be reactive, and facilitates access to information for crucial practical and academic purposes when it comes to changing the attitude of managers, so that their actions become increasingly more proactive. OBJECTIVE: To identify, classify, analyze, and discuss the existing literature related to the topic, produced from 2008 to 2020, besides contributing to a broader understanding of risk management in occupational safety. METHODS: We did a systematic literature mapping. The research process was documented starting by the planning stage. Afterwards, the focus was on research conduction and information synthesis. RESULTS: Knowledge systematization and stratification about OHS risk management through various perspectives to identify, analyze and manage risks in the workplace. Were identified 37 tools for identifying and analyzing risks, management-related practices and future research trends. CONCLUSIONS: The set of tools and management practices identified can be used as a support for decision making in the selection process of tools and practices to reduce risks and improve occupational safety. Also, the results can help target future research.
Resumo Introdução: realizar a predição de doenças relacionadas ao trabalho é um desafio às organizações e ao poder público. Com as técnicas de aprendizado de máquina (AM), é possível identificar fatores determinantes para a ocorrência de uma doença ocupacional, visando direcionar ações mais efetivas à proteção dos trabalhadores. Objetivo: predizer, a partir da comparação de técnicas de AM, os fatores com maior influência para a ocorrência de dermatite ocupacional. Métodos: desenvolveu-se um código em linguagem R e uma análise descritiva dos dados e identificaram-se os fatores de influência de acordo com a técnica de AM que demonstrou melhor desempenho. O banco de dados foi disponibilizado pelo Serviço de Dermatologia Ocupacional da Fundação Oswaldo Cruz e contém informações de trabalhadores que apresentaram alterações cutâneas sugestivas de dermatite ocupacional no período de 2000-2014. Resultados: as técnicas com melhor desempenho foram: neural network, random forest, support vector machine e naive Bayes. As variáveis sexo, escolaridade e profissão foram as mais adequadas para os modelos de previsão de dermatite ocupacional. Conclusão: as técnicas de AM possibilitam predizer os fatores que influenciam a segurança e a saúde dos trabalhadores, os parâmetros que subsidiam a implantação de procedimentos e as políticas mais efetivas para prevenir a dermatite ocupacional.
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