Cargando…

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

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 di...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Hongbo, Tang, Zhifeng, Yang, Yongli, Weng, Dong, Sun, Gao, Duan, Zhiwen, Chen, Jie
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760532/
https://www.ncbi.nlm.nih.gov/pubmed/19785771
http://dx.doi.org/10.1186/1471-2458-9-366
_version_ 1782172751968075776
author Liu, Hongbo
Tang, Zhifeng
Yang, Yongli
Weng, Dong
Sun, Gao
Duan, Zhiwen
Chen, Jie
author_facet Liu, Hongbo
Tang, Zhifeng
Yang, Yongli
Weng, Dong
Sun, Gao
Duan, Zhiwen
Chen, Jie
author_sort Liu, Hongbo
collection PubMed
description 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
format Text
id pubmed-2760532
institution National Center for Biotechnology Information
language English
publishDate 2009
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-27605322009-10-13 Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study Liu, Hongbo Tang, Zhifeng Yang, Yongli Weng, Dong Sun, Gao Duan, Zhiwen Chen, Jie BMC Public Health Research Article 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. BioMed Central 2009-09-29 /pmc/articles/PMC2760532/ /pubmed/19785771 http://dx.doi.org/10.1186/1471-2458-9-366 Text en Copyright © 2009 Liu et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Hongbo
Tang, Zhifeng
Yang, Yongli
Weng, Dong
Sun, Gao
Duan, Zhiwen
Chen, Jie
Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study
title Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study
title_full Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study
title_fullStr Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study
title_full_unstemmed Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study
title_short Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study
title_sort identification and classification of high risk groups for coal workers' pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760532/
https://www.ncbi.nlm.nih.gov/pubmed/19785771
http://dx.doi.org/10.1186/1471-2458-9-366
work_keys_str_mv AT liuhongbo identificationandclassificationofhighriskgroupsforcoalworkerspneumoconiosisusinganartificialneuralnetworkbasedonoccupationalhistoriesaretrospectivecohortstudy
AT tangzhifeng identificationandclassificationofhighriskgroupsforcoalworkerspneumoconiosisusinganartificialneuralnetworkbasedonoccupationalhistoriesaretrospectivecohortstudy
AT yangyongli identificationandclassificationofhighriskgroupsforcoalworkerspneumoconiosisusinganartificialneuralnetworkbasedonoccupationalhistoriesaretrospectivecohortstudy
AT wengdong identificationandclassificationofhighriskgroupsforcoalworkerspneumoconiosisusinganartificialneuralnetworkbasedonoccupationalhistoriesaretrospectivecohortstudy
AT sungao identificationandclassificationofhighriskgroupsforcoalworkerspneumoconiosisusinganartificialneuralnetworkbasedonoccupationalhistoriesaretrospectivecohortstudy
AT duanzhiwen identificationandclassificationofhighriskgroupsforcoalworkerspneumoconiosisusinganartificialneuralnetworkbasedonoccupationalhistoriesaretrospectivecohortstudy
AT chenjie identificationandclassificationofhighriskgroupsforcoalworkerspneumoconiosisusinganartificialneuralnetworkbasedonoccupationalhistoriesaretrospectivecohortstudy