# 2004 British Occupational Hygiene Society Published by Oxford University Press doi:10.1093/glycob/meh064 Artificial Neural Networks and Job-specific Modules to Assess Occupational Exposure

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