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Annals of Occupational Hygiene Advance Access published online on September 20, 2004
Annals of Occupational Hygiene, doi:10.1093/annhyg/meh064
Copyright © 2004 by the British Occupational Hygiene Society.
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Received January 2, 2002
Accepted February 2, 2004

Article

Artificial Neural Networks and Job-specific Modules to Assess Occupational Exposure

Jim Black 1*, Geza Benke 1, Kate Smith 2, and Lin Fritschi 3

1 Department of Epidemiology and Preventive Medicine, Monash University, MMS Alfred Hospital, Prahran, Victoria 3181, Australia
2 School of Business Systems, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
3 Department of Public Health, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia

* To whom correspondence should be addressed. E-mail: jim.black{at}med.monash.edu.au.


  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 ~60% by identifying negative JSMs. The use of artificial neural networks shows promise in future applications to occupational assessment by JSMs and expert assessment.


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