Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study
暂无分享,去创建一个
Hongbo Liu | Gao Sun | Jie Chen | Hongbo Liu | Jie Chen | D. Weng | Zhifeng Tang | Yongli Yang | G. Sun | Zhiwen Duan | Dong Weng | Zhifeng Tang | Yongli Yang | Zhiwen Duan | Z. Duan
[1] Sen-lin Liu,et al. THE ESTIMATION OF THE NUMBER OF UNDERGROUND COAL MINERS AND THE ANNUAL DOSE TO COAL MINERS IN CHINA , 2007, Health physics.
[2] Jeffery G Blodgett,et al. A visual method for determining variable importance in an artificial neural network model: An empirical benchmark study , 2003 .
[3] C. Bridges,et al. Advanced cases of coal workers' pneumoconiosis--two counties, Virginia, 2006. , 2006, MMWR. Morbidity and mortality weekly report.
[4] M D Attfield,et al. An investigation into the relationship between coal workers' pneumoconiosis and dust exposure in U.S. coal miners. , 1992, American Industrial Hygiene Association journal.
[5] R. Reger,et al. Occupational safety and health implications of increased coal utilization. , 1979, Environmental health perspectives.
[6] W Karwowski,et al. A neural network-based system for classification of industrial jobs with respect to risk of low back disorders due to workplace design. , 1997, Applied ergonomics.
[7] L. C. Kenny,et al. Estimation of the Risk of Contracting Pneumoconiosis in the UK Coal Mining Industry , 2002 .
[8] Michael Green,et al. Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room , 2006, Artif. Intell. Medicine.
[9] D. Heederik,et al. Characterization of dust exposure for the study of chronic occupational lung disease: a comparison of different exposure assessment strategies. , 2000, American journal of epidemiology.
[10] M D Attfield,et al. Prevalence of pneumoconiosis and its relationship to dust exposure in a cohort of U.S. bituminous coal miners and ex-miners. , 2013, American journal of industrial medicine.
[11] P. Kinnear. The politics of coal dust: industrial campaigns for the regulation of dust disease in Australian coal mining, 1939-49. , 2001, Labour history.
[12] Farid E Ahmed,et al. Molecular Cancer BioMed Central Review , 2005 .
[13] Jouko Lampinen,et al. Bayesian approach for neural networks--review and case studies , 2001, Neural Networks.
[14] M D Attfield,et al. Rapidly progressive coal workers’ pneumoconiosis in the United States: geographic clustering and other factors , 2005, Occupational and Environmental Medicine.
[15] Da‐hong Wang,et al. The current state of workers' pneumoconiosis in relationship to dusty working environments in Okayama Prefecture, Japan. , 2002, Acta medica Okayama.
[16] B. Moen,et al. High prevalence of respiratory symptoms among workers in the development section of a manually operated coal mine in a developing country: A cross sectional study , 2007, BMC public health.
[17] D. F. Scott,et al. Disease and Illness in U.S. Mining, 1983–2001 , 2004, Journal of occupational and environmental medicine.
[18] K. Lebecki,et al. Occurrence and prevention of coal miners' pneumoconiosis in Poland. , 1999, American journal of industrial medicine.
[19] 이수정. 해외산업간호정보 - 미국 산업안전보건연구원(National Institute for Occupational Safety and Health) 소개 , 2009 .
[20] D. Giannella‐Neto,et al. PTTG expression in different experimental and human prolactinomas in relation to dopaminergic control of lactotropes , 2007, Molecular Cancer.
[21] M. Attfield,et al. Surveillance data on US coal miners' pneumoconiosis, 1970 to 1986. , 1992, American journal of public health.
[22] J. Murray,et al. Respiratory outcomes among South African coal miners at autopsy. , 2005, American journal of industrial medicine.
[23] K. Moons,et al. A simple diagnostic model for ruling out pneumoconiosis among construction workers , 2007, Occupational and Environmental Medicine.
[24] Lin Fritschi,et al. Artificial neural networks and job-specific modules to assess occupational exposure. , 2004, The Annals of occupational hygiene.
[25] D. Linkens,et al. Application of artificial intelligence to the management of urological cancer. , 2007, The Journal of urology.
[26] J. Weeks. The Mine Safety and Health Administration's criterion threshold value policy increases miners' risk of pneumoconiosis. , 2006, American journal of industrial medicine.
[27] Xin-Qiu Yao,et al. A dynamic Bayesian network approach to protein secondary structure prediction , 2008, BMC Bioinformatics.
[28] MD EL Petsonk,et al. Pneumoconiosis prevalence among working coal miners examined in federal chest radiograph surveillance programs--United States, 1996-2002. , 2003, MMWR. Morbidity and mortality weekly report.
[29] V. Castranova,et al. Silicosis and coal workers' pneumoconiosis. , 2000, Environmental health perspectives.
[30] Parobeck. Effect of the 2.0 mg/m3 coal mine dust standard on underground environmental dust levels. , 1975, American Industrial Hygiene Association journal.
[31] W. Baxt. Application of artificial neural networks to clinical medicine , 1995, The Lancet.
[32] M. Onder,et al. Evaluation of occupational exposures to respirable dust in underground coal mines. , 2009, Industrial health.