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 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.
Black
Jim
J
Department of Epidemiology and Preventive Medicine, Monash University, MMS Alfred Hospital, Prahran, Victoria 3181, Australia. jim.black@med.monash.edu.au
Benke
Geza
G
Smith
Kate
K
Fritschi
Lin
L
D001554:Benzene / Q000032:analysis; D006801:Humans; D008228:Lymphoma, Non-Hodgkin / Q000139:chemically induced / Q000453:epidemiology; D016571:Neural Networks, Computer; D009517:New South Wales / Q000453:epidemiology; D009784:Occupational Diseases / Q000139:chemically induced / Q000453:epidemiology; D016273:Occupational Exposure / Q000032:analysis*
D001554:Benzene