Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study |
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| Authors: | Hongbo Liu Zhifeng Tang Yongli Yang Dong Weng Gao Sun Zhiwen Duan and Jie Chen |
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| Institution: | (1) Division of Pneumoconiosis, School of Public Health, China Medical University, 92 North 2ndRoad, 110001 Shenyang, PR China;(2) Department of Health Statistics, School of Public Health, China Medical University, 92 North 2nd Road, 110001 Shenyang, PR China;(3) Division of Occupational Disease, General Hospital of Tiefa Colliery, Diaobingshan, 112700 Liaoning Province, PR China |
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| Abstract: | Background Coal workers' pneumoconiosis (CWP) is a preventable, but not fully curable occupational lung disease. More and more coal miners
are likely to be at risk of developing CWP owing to an increase in coal production and utilization, especially in developing
countries. Coal miners with different occupational categories and durations of dust exposure may be at different levels of
risk for CWP. It is necessary to identify and classify different levels of risk for CWP in coal miners with different work
histories. In this way, we can recommend different intervals for medical examinations according to different levels of risk
for CWP. Our findings may provide a basis for further emending the measures of CWP prevention and control. |
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