Identification of high-risk beneficiaries in private healthcare insurance.

The objective of this study was to apply the Knowledge Discovery in Databases process to find out if beneficiaries of a private healthcare insurance would belong, at least once, to the 'very high cost' and 'complex cases' groups throughout the 12 months after the month when algorithms were applied. Datasets were built containing information on beneficiaries' effective use of their health plan, as well as their characteristics. Five machine learning algorithms were used, namely Random forest, Extra tree, Xgboost, Naive bayes and K-nearest neighbor. The K-nearest neighbor algorithm had a recall rate of 81.12%, 83.77% precision and an Area Under the Curve (AUC) value of 0.9045. The study also revealed that categorization occurs, on average, 8.11 months before a beneficiary entering, for the first time, a high-risk group, considering the dataset classification from January 2019 to June 2020.

Impact of climate change on occupational health and safety: A review of methodological approaches.

BACKGROUND: The working population is exposed daily to unavoidable climatic conditions due to their occupational settings. Effects of the weather such as rain, heat, and air pollution may increase the risk of diseases, injuries, accidents, and even death during labor. OBJECTIVE: This paper aims to summarize the impacts of climate change on workers' health, safety and performance, identifying the risks, affected workplaces and the range of methodological approaches used to assess this problem. METHODS: A thorough systematic mapping was conducted in seven scientific international databases: Emerald, IEEE Xplore, Science Direct, Scielo, Scopus, SpringerLink, and Web of Science. Three research questions guided the extraction process resulting in 170 articles regarding the impacts of climate change on occupational health and safety. RESULTS: We found an accentuated trend in observational studies applying primary and secondary data collection. Many studies focused on the association between rising temperatures and occupational hazards, mainly in outdoor work settings such as agriculture. The variation of temperature was the most investigated impact of climate change. CONCLUSIONS: We established a knowledge base on how to explore the impacts of climate change on workers' well-being and health. Researchers and policymakers benefit from this review, which explores the suitable methods found in the literature and highlights the most recurring risks and their consequences to occupational health and safety.

Uso de técnicas de aprendizado de máquina para classificação de fatores que influenciam a ocorrência de dermatites ocupacionais / Using machine learning techniques to classify factors that influence the occurrence of occupational dermatitis

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.

Abstract Introduction: to predict work related diseases is a challenge for organizations and the governmental authorities. By means of machine learning (ML) techniques it is possible to identify factors that determine the occurrence of an occupational disease, aiming at taking more effective actions to protect workers. Objective: to predict, by comparing ML techniques, the factors which highly influence the occurrence of occupational dermatitis. Methods: we developed a code in R language and a descriptive analysis of the data and identified the influence factors according to the ML technique that presented the best performance. The database was made available by the Occupational Dermatology Service of Oswaldo Cruz Foundation and assembles information of the workers who experienced cutaneous alterations suggestive of occupational dermatitis between 2000-2014. Results: the techniques which presented the best performance were: neural network, random forest, support vector machine, and naive Bayes. Sex, schooling, and profession were the most adequate variables for the occupational dermatitis prediction models. Conclusion: ML techniques allowed to predict the factors that influence the workers' safety and health, as well as the parameters that subsidize the procedures implementation, and the most effective policies to prevent occupational dermatitis.