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Annals of Occupational Hygiene Advance Access originally published online on September 20, 2004
Annals of Occupational Hygiene 2004 48(7):595-600; doi:10.1093/annhyg/meh064
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© British Occupational Hygiene Society Published by Oxford University Press;

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

JIM BLACK1,*, GEZA BENKE1, KATE SMITH2 and LIN FRITSCHI3

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

* Author to whom correspondence should be addressed. Tel: +61-3-9342-8897; fax: +61-3-9342-7277; e-mail:jim.black{at}med.monash.edu.au

Received 2 January 2002; in final form 2 February 2004

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.

Keywords: artificial neural networks • epidemiology • exposure assessment • job-specific modules


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