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

J. Black, G. Benke, K. Smith, Lin Fritschi

Research output: Contribution to journalArticle

18 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)595-6000
JournalThe Annals of Occupational Hygiene
Issue number7
Publication statusPublished - 2004

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