Probabilistic Machine Learning with Low-Cost Sensor Networks for Occupational Exposure Assessment and Industrial Hygiene Decision Making.

Patton, Andrew N; Medvedovsky, Konstantin; Zuidema, Christopher; Peters, Thomas M; Koehler, Kirsten

Patton, Andrew N; Medvedovsky, Konstantin; Zuidema, Christopher; Peters, Thomas M; Koehler, Kirsten.

Afiliação

Patton AN; Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Medvedovsky K; QR Analytics, Washington DC, USA.
Zuidema C; Department of Environmental and Occupational Health Sciences, University of Washington Hans Rosling Center for Population Health, Seattle, WA, USA.
Peters TM; Department of Occupational and Environmental Health, University of Iowa, Iowa City, IA, USA.
Koehler K; Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Ann Work Expo Health ; 66(5): 580-590, 2022 06 06.

Article em En | MEDLINE | ID: mdl-34849566

Assuntos

Exposição Ocupacional; Saúde Ocupacional; Tomada de Decisões; Monitoramento Ambiental; Humanos; Aprendizado de Máquina; Instalações Industriais e de Manufatura; Exposição Ocupacional/análise

Palavras-chave

carbon monoxide; exposure assessment; machine learning; occupational exposure assessment; sensor networks

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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 12_ODS3_hazardous_contamination / 1_ASSA2030 / 2_ODS3 Base de dados: MEDLINE Assunto principal: Exposição Ocupacional / Saúde Ocupacional Tipo de estudo: Health_economic_evaluation / Prognostic_studies Aspecto: Determinantes_sociais_saude Limite: Humans Idioma: En Revista: Ann Work Expo Health Ano de publicação: 2022 Tipo de documento: Article

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