Artificial neural networks and job-specific modules to assess occupational exposure

The Annals of Occupational Hygiene
Jim BlackLin Fritschi

Abstract

Job-specific modules (JSMs) were used to collect information for expert retrospective exposure assessment in a community-based non-Hodgkins Lymphoma study in New South Wales, Australia. Using exposure assessment by a hygienist, artificial neural networks were developed to predict overall and intermittent benzene exposure among the module of tanker drivers. Even with a small data set (189 drivers), neural networks could assess benzene exposure with an average of 90% accuracy. By appropriate choice of cutoff (decision threshold), the neural networks could reliably reduce the expert's workload by approximately 60% by identifying negative JSMs. The use of artificial neural networks shows promise in future applications to occupational assessment by JSMs and expert assessment.

Citations

Dec 29, 2010·The Annals of Occupational Hygiene·Farman A Moayed, Richard L Shell
Oct 27, 2015·Indian Journal of Occupational and Environmental Medicine·Maryam FarhadianEbrahim Darvishi
Nov 30, 2014·International Archives of Occupational and Environmental Health·Mohsen AliabadiEbrahim Darvishi
Jul 13, 2019·Current Environmental Health Reports·Jean-François Sauvé, Melissa C Friesen
Sep 26, 2009·Toxicology Mechanisms and Methods·Yan-Ning LiZai-Biao Zhang

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