Prediction of the length of service at the onset of coal workers’ pneumoconiosis based on neural network

Reference

• Mandrioli D, Schlunssen V, Adam B, et al. WHO/ILO work-related burden of disease and injury: protocol for systematic reviews of occupational exposure to dusts and/or fibres and of the effect of occupational exposure to dusts and/or fibres on pneumoconiosis. Environ Int. 2018;119:174–185. doi:10.1016/j.envint.2018.06.005.
   PubMed Web of Science ®Google Scholar
• Yorio PL, Laney AS, Halldin CN, et al. Interstitial lung diseases in the US Mining Industry: using MSHA Data to examine trends and the prevention effects of compliance with health regulations, 1996-2015. Risk Anal. 2018;38(9):1962–1971. doi:10.1111/risa.13000.
   PubMed Web of Science ®Google Scholar
• Cheng BW, Su M. International incidence trend of coal workers' pneumoconiosis and silicosis. Chin J Ind Hygiene Occupat Dis. 2019;37(1):75–78.
   Google Scholar
• Han S, Chen H, Harvey M-A, Stemn E, Cliff D. Focusing on coal workers' lung diseases: a comparative analysis of China, Australia, and the United States. Int J Env Res Pub He. 2018;15(11):2565.
   Web of Science ®Google Scholar
• Laura R, Cara NH, A. Scott L, David JB. Coal miner participation in a job transfer program designed to prevent progression of pneumoconiosis, United States, 1986–2016. Arch Environ Occup Health. 2018;61(2):61–66. doi:10.3200/AEOH.61.2.61-66.
   Google Scholar
• Moreno T, Trechera P, Querol X, et al. Trace element fractionation between PM10 and PM2.5 in coal mine dust: implications for occupational respiratory health. Int J Coal Geol. 2019;203:52–59. doi:10.1016/j.coal.2019.01.006.
   Web of Science ®Google Scholar
• Han F, Chen YQ, Wu B, Kang N, Zhang SY. Occupational health risk assessment of coal dust in coal industry chain. Chin J Ind Hygiene Occupat Dis. 2018;36(4):291–294.
   Google Scholar
• Varona M, Ibanez-Pinilla M, Briceno L, et al. Evaluation of the exposure to coal dust and prevalence of pneumoconiosis in underground mining in three Colombian departments. Biomedica 2018;38(4):125–136.
   Web of Science ®Google Scholar
• Zlotkowska R, Mroczek A. Assessment of the incidence and risk factors of coal-worker's pneumoconiosis (CWP) in Silesia, Poland. Eur Respir J. 2018;52:311–326.
   Web of Science ®Google Scholar
• Wang XX, Zhang HD, Wang XM. The analysis of the epidemiological characteristics of pneumoconiosis notified in Chongqing from 2011 to 2015. Chin J Ind Hygiene Occupat Dis. 2018;36(3):194–197.
   Google Scholar
• Cui FT. Study on the prevalence, prediction and prevention benefits of coal worker's pneumoconiosis in Huaibei mining group. North China University of Science and Technology; 2016.
   Google Scholar
• Shen FH. Prevalence and incidence prediction of coal workers’ pneumoconiosis and economic benefits analysis for its prevention in Datong Coal Mine Group. China Medical University; 2013.
   Google Scholar
• Wang T, Li Y, Zhu M, et al. Association analysis identifies new risk loci for coal workers' pneumoconiosis in Han Chinese men. Toxicol Sci. 2018;163(1):206–213. doi:10.1093/toxsci/kfy017.
   PubMed Web of Science ®Google Scholar
• Li Y, Xian W, Xu H, Sun J, Han B, Liu H. Time trends and future prediction of coal worker’s pneumoconiosis in opencast coal mine in China based on the APC model. BMC Public Health. 2018;18(1):1010. doi:10.1186/s12889-018-5937-0.
   PubMedGoogle Scholar
• Han L, Gao Q, Yang J, et al. Survival analysis of Coal Workers’ Pneumoconiosis (CWP) patients in a state-owned mine in the East of China from 1963 to 2014. Int J Environ Res Public Health. 2017;14(5):489.
   Web of Science ®Google Scholar
• Wu J, Wang X, Guo X, Wang G, Su Y, Zhou L. Forecasting incidence seniority of coal workers’ pneumoconiosis based on BP neural network. Paper presented at Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012); 2013; vol. 4; pp. 559–564.
   Google Scholar
• Wang X, Wu J, Yin S, Wang G, Guo Z. Forecasting Incidence Age of Coal Workers’ Pneumoconiosis Based on BP Neural Networks. Paper presented at Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012); 2013;vol. 1; pp. 657–664.
   Google Scholar
• Guo NL, Han B, Liu H, et al. Estimates and predictions of coal workers’ pneumoconiosis cases among redeployed coal workers of the fuxin mining industry group in China: a historical cohort study. Plos One. 2016;11(2):e0148179. doi:10.1371/journal.pone.0148179.
   PubMed Web of Science ®Google Scholar
• Shen F, Yuan J, Sun Z, et al. Risk identification and prediction of coal workers’ pneumoconiosis in Kailuan colliery group in China: a historical cohort study. Plos One. 2013;8(12):e82181. doi:10.1371/journal.pone.0082181.
   PubMed Web of Science ®Google Scholar
• Hongbo L, Zhifeng T, Dong W, et al. Prevalence Characteristics and prediction of coal workers’ pneumoconiosis in the Tiefa Colliery in China. Ind Health. 2009;47:369–375.
   PubMed Web of Science ®Google Scholar
• Wu J, Shao H, Su Y, et al. The application of BP neural network model in the prediction of coal worker's pneumoconiosis incidence seniority. J Chem Pharm Res. 2014; 6(3):141–148.
   Google Scholar
• Xian W, Han B, Xia L, et al. Focusing on the premature death of redeployed miners in China: an analysis of cause-of-death information from non-communicable diseases. Global Health. 2019;15:7.
   PubMed Web of Science ®Google Scholar
• Jin Y, Fan Jing G, Pang J, et al. Risk of active pulmonary tuberculosis among patients with coal workers' pneumoconiosis: a case-control study in China. Biomed Environ Sci. 2018;31(6):448.
   PubMed Web of Science ®Google Scholar
• Han L, Yao W, Bian Z, et al. Characteristics and trends of pneumoconiosis in the Jiangsu Province, China. Int J Env Res Pub He. 2019;16(3):2006–2017.
   Web of Science ®Google Scholar
• Graber JM. Application of the Delphi method to reduce disability and mortality from coal mine dust lung disease in China; a new approach to an old problem. Occup Environ Med. 2018;75(9):615–616. doi:10.1136/oemed-2018-105075.
   PubMed Web of Science ®Google Scholar
• Fengtao C, Jie X, Xin-Ping D, et al. Analysis on epidemic situation of pneumoconiosis in Huaibei Mining Group from 1963-2012. Occup and Health 2014;30(24):3519–3520.
   Google Scholar
• Cui K, Shen F, Han B, Liu H, Chen J. Establishment and application of an index system for prevention of coal workers' pneumoconiosis: a Delphi and analytic hierarchy process study in four state-owned coal enterprises of China. Occup Environ Med. 2018;75(9):654–660. doi:10.1136/oemed-2017-104909.
   PubMed Web of Science ®Google Scholar
• Halldin CN, Wolfe AL, Laney AS. Comparative respiratory morbidity of former and current US coal miners. Am J Public Health. 2015;105(12):2576–2577. doi:10.2105/AJPH.2015.302897.
   PubMed Web of Science ®Google Scholar
• Cummings KJ, Johns DO, Mazurek JM, Hearl FJ, Weissman DN. NIOSH's Respiratory Health Division: 50 years of science and service. Arch Environ Occup H. 2019;74(1–2):15–29. doi:10.1080/19338244.2018.1532387.
   PubMed Web of Science ®Google Scholar
• Laney AS, Attfield MD. Examination of potential sources of bias in the US coal workers' health surveillance program. Am J Public Health. 2014;104(1):165–170. doi:10.2105/AJPH.2012.301051.
   PubMed Web of Science ®Google Scholar
• Du WZ, Wang G, Wang Y, etc. Thermal degradation of bituminous coal with both model-free and model-fitting methods. Appl Therm Eng. 2019; 152:169–174. doi:10.1016/j.applthermaleng.2019.02.092.
   Web of Science ®Google Scholar
• Varona M, Ibanez-Pinilla M, Briceno L, et al. Evaluacion de la exposicion al polvo de carbon y de silice en sitios de mineria subterranea en tres departamentos de Colombia. Biomedica. 2018;38(4):467–478. doi:10.7705/biomedica.v38i4.4183.
   PubMedGoogle Scholar