Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>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,...

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Main Authors: Sun Gao (Author), Weng Dong (Author), Yang Yongli (Author), Tang Zhifeng (Author), Liu Hongbo (Author), Duan Zhiwen (Author), Chen Jie (Author)
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Published: BMC, 2009-09-01T00:00:00Z.
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042 |a dc 
100 1 0 |a Sun Gao  |e author 
700 1 0 |a Weng Dong  |e author 
700 1 0 |a Yang Yongli  |e author 
700 1 0 |a Tang Zhifeng  |e author 
700 1 0 |a Liu Hongbo  |e author 
700 1 0 |a Duan Zhiwen  |e author 
700 1 0 |a Chen Jie  |e author 
245 0 0 |a Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study 
260 |b BMC,   |c 2009-09-01T00:00:00Z. 
500 |a 10.1186/1471-2458-9-366 
500 |a 1471-2458 
520 |a <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>The study was performed using longitudinal retrospective data in the Tiefa Colliery in China. A three-layer artificial neural network with 6 input variables, 15 neurons in the hidden layer, and 1 output neuron was developed in conjunction with coal miners' occupational exposure data. Sensitivity and ROC analyses were adapted to explain the importance of input variables and the performance of the neural network. The occupational characteristics and the probability values predicted were used to categorize coal miners for their levels of risk for CWP.</p> <p>Results</p> <p>The sensitivity analysis showed that influence of the duration of dust exposure and occupational category on CWP was 65% and 67%, respectively. The area under the ROC in 3 sets was 0.981, 0.969, and 0.992. There were 7959 coal miners with a probability value < 0.001. The average duration of dust exposure was 15.35 years. The average duration of ex-dust exposure was 0.69 years. Of the coal miners, 79.27% worked in helping and mining. Most of the coal miners were born after 1950 and were first exposed to dust after 1970. One hundred forty-four coal miners had a probability value ≥0.1. The average durations of dust exposure and ex-dust exposure were 25.70 and 16.30 years, respectively. Most of the coal miners were born before 1950 and began to be exposed to dust before 1980. Of the coal miners, 90.28% worked in tunneling.</p> <p>Conclusion</p> <p>The duration of dust exposure and occupational category were the two most important factors for CWP. Coal miners at different levels of risk for CWP could be classified by the three-layer neural network analysis based on occupational history.</p> 
546 |a EN 
690 |a Public aspects of medicine 
690 |a RA1-1270 
655 7 |a article  |2 local 
786 0 |n BMC Public Health, Vol 9, Iss 1, p 366 (2009) 
787 0 |n http://www.biomedcentral.com/1471-2458/9/366 
787 0 |n https://doaj.org/toc/1471-2458 
856 4 1 |u https://doaj.org/article/b7fccbb869b24b4cafffdd83d34e82d5  |z Connect to this object online.