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基于职业史的人工神经网络对煤工尘肺高危人群的识别与分类:一项回顾性队列研究。

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

机构信息

Division of Pneumoconiosis, School of Public Health, China Medical University, Shenyang, PR China.

出版信息

BMC Public Health. 2009 Sep 29;9:366. doi: 10.1186/1471-2458-9-366.

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.

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 > or =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.

摘要

背景

煤工尘肺(CWP)是一种可预防但无法完全治愈的职业性肺部疾病。由于煤炭产量和利用率的增加,尤其是在发展中国家,越来越多的煤矿工人可能面临罹患 CWP 的风险。不同职业类别和粉尘暴露时间的煤矿工人可能面临不同程度的 CWP 风险。有必要根据不同的 CWP 风险水平来识别和分类煤矿工人的不同风险级别。这样,我们就可以根据不同的 CWP 风险水平推荐不同的体检间隔。我们的研究结果可能为进一步修订 CWP 防治措施提供依据。

方法

本研究采用中国铁法煤矿的纵向回顾性数据。结合煤矿工人职业暴露数据,开发了一个具有 6 个输入变量、隐藏层 15 个神经元和 1 个输出神经元的三层人工神经网络。采用敏感性和 ROC 分析来解释输入变量的重要性和神经网络的性能。根据职业特征和预测的概率值对煤矿工人进行分类,以评估其 CWP 风险水平。

结果

敏感性分析表明,粉尘暴露时间和职业类别的影响分别为 65%和 67%。3 组的 ROC 曲线下面积分别为 0.981、0.969 和 0.992。有 7959 名矿工的概率值<0.001。平均粉尘暴露时间为 15.35 年,平均脱离粉尘暴露时间为 0.69 年。79.27%的矿工从事辅助和采煤工作。大多数矿工出生于 1950 年以后,1970 年以后首次接触粉尘。144 名矿工的概率值≥0.1。粉尘暴露时间和脱离粉尘暴露时间的平均值分别为 25.70 年和 16.30 年。大多数矿工出生于 1950 年以前,1980 年以前开始接触粉尘。90.28%的矿工从事隧道工作。

结论

粉尘暴露时间和职业类别是 CWP 的两个最重要因素。基于职业史,通过三层神经网络分析可对不同 CWP 风险水平的煤矿工人进行分类。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f06/2760532/11b5c100a10c/1471-2458-9-366-1.jpg

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