基于反向传播神经网络构建煤工尘肺的患病风险预测模型——一项以医院为基础的病例对照研究

杨雨橦; 田清华; 安琪; 郝建光; 王剑茹; 武姣; 李怡淳; 李杨; 王庆尧; 李宇星; 雷立健; 罗铭忠

doi:10.16462/j.cnki.zhjbkz.2024.08.015

基于反向传播神经网络构建煤工尘肺的患病风险预测模型——一项以医院为基础的病例对照研究

doi: 10.16462/j.cnki.zhjbkz.2024.08.015

杨雨橦^{1, 2},
田清华^{1, 2},
安琪^{1, 2},
郝建光³,
王剑茹⁴,
武姣⁴,
李怡淳^{1, 2},
李杨^{1, 2},
王庆尧^{1, 2},
李宇星^{1, 2},
雷立健^{1, 2, 5, ,},
罗铭忠^6, ,

1.
山西医科大学公共卫生学院流行病学教研室, 太原 030001
2.
煤炭环境致病与防治教育部重点实验室, 太原 030001
3.
山西省第二人民医院职业病中毒科, 太原 030012
4.
山西省第二人民医院医教科, 太原 030012
5.
国家卫生健康委员会尘肺病重点实验室, 太原 030001
6.
山西省第二人民医院院长办公室，太原 030012

基金项目:

山西省“四个一批”科技兴医创新计划 2021XM43

煤炭环境致病与防制教育部重点实验室开放课 (MEKLCEPP/SXMU-202303)

详细信息

通讯作者:
罗铭忠，E-mail: lmz7344@163.com

雷立健，E-mail: wwdlijian@sxmu.edu.cn

中图分类号: R135.2
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出版历程
- 收稿日期: 2023-12-07
- 修回日期: 2024-05-15
- 网络出版日期: 2024-09-29
- 刊出日期: 2024-08-10

A neural network risk prediction model of coal workers′ pneumoconiosis-a hospital-based case-control study

YANG Yutong^{1, 2},
TIAN Qinghua^{1, 2},
AN Qi^{1, 2},
HAO Jianguang³,
WANG Jianru⁴,
WU Jiao⁴,
LI Yichun^{1, 2},
LI Yang^{1, 2},
WANG Qingyao^{1, 2},
LI Yuxing^{1, 2},
LEI Lijian^{1, 2, 5
, ,},
LUO Mingzhong^{6
, ,}

1.
Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
2.
MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Taiyuan 030001, China
3.
Department of Occupational Diseases and Poisoning, The Second People′s Hospital of Shanxi Province, Taiyuan 030012, China
4.
Department of Medical and Education, The Second People′s Hospital of Shanxi Province, Taiyuan 030012, China
5.
NHC Key Laboratory of Pneumoconiosis, Taiyuan 030001, China
6.
Office of the President, The Second People′s Hospital of Shanxi Province, Taiyuan 030012, China

Funds:

The "Four Batch" of Technology-Driven Medical Innovation Plan in Shanxi Province, China 2021XM43

Open Project of MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, China (MEKLCEPP/SXMU-202303)

More Information

Corresponding author: LEI Lijian, E-mail: wwdlijian@sxmu.edu.cn; LUO Mingzhong, E-mail: lmz7344@163.com<

摘要

摘要: 目的旨在构建高性能煤工尘肺(coal workers′ pneumoconiosis, CWP)风险预测模型，促进CWP的早期预防。方法基于医院的病例对照研究，收集2017―2022年山西省某职业病医院的CWP患者和同期矿工非CWP患者病例资料，建立CWP数据库，采用随机森林筛选特征变量，基于反向传播(back propagation, BP)神经网络和logistic回归分析模型分别构建CWP预测模型，并利用受试者工作特征(receiver operating characteristic, ROC)曲线评价2个模型的CWP预测能力。结果 BP神经网络模型灵敏度为88.6%，特异度为87.6%，准确率为87.12%；变量正态化重要性结果显示，影响煤矿工人发生CWP最重要的因素有1秒通气率(forceful expiratory volume in 1 second/ forceful vital capacity, FEV1/FVC)、工龄、工种。Logistic回归分析模型结果显示灵敏度80.7%，特异度84.1%，准确率82.7%。BP神经网络模型ROC曲线下面积(area under the curve, AUC)(AUC=0.918，95% CI：0.903~0.964)高于logistic回归分析模型(AUC=0.802，95% CI：0.750~0.850)，BP神经网络模型的预测性能优于logistic回归分析模型。结论 BP神经网络的预测性能高于logistic回归分析模型，将BP神经网络应用在CWP预测上有更高的准确性。FEV1/FVC、工龄、工种是影响煤矿工人发生CWP的重要因素。
- 反向传播神经网络 /
- 煤工尘肺 /
- Logistic回归分析模型 /
- 预测模型
Abstract: Objective This study aims to construct a high-efficiency coal workers′ pneumoconiosis (CWP) risk prediction model to promote early prevention of CWP. Methods We conducted a case-control study based on hospital records, collected case data of coal workers diagnosed with CWP and non-CWP in an occupational disease hospital in Shanxi Province from 2017 to 2022 and established a database of CWP. Random forest method was used to screen the characteristic variables. The CWP prediction model was constructed based on back propagation (BP) neural network and Logistic regression respectively, and the CWP prediction ability of the two models was evaluated by receiver operating characteristic (ROC). Results The BP neural network model demonstrated a sensitivity of 88.6%, a specificity of 87.6%, and an accuracy rate of 87.12%. Based on variable normalization importance analysis, the most influential factors for CWP prevalence in coal workers were forceful expiratory volume in 1 second/ forceful vital capacity (FEV1/FVC), working age and work type. The logistic regression model showed a sensitivity of 80.7%, a specificity of 84.1%, and an accuracy rate of 82.7%. The BP neural network model exhibited a higher area under the curve (AUC) value (AUC=0.918, 95% CI: 0.903-0.964) compared to the logistic regression model (AUC=0.802, 95% CI: 0.750-0.850), indicating superior predictive performance. Conclusions The BP neural network model provides better predictive performance compared to the logistic regression model, and applying the BP neural network to CWP prediction has higher accuracy. FEV1/FVC, working age and work type are identified as significant factors influencing the occurrence of CWP in coal workers.
- Back propagation neural network /
- Coal workers′ pneumoconiosis /
- Logistic regression /
- Prediction model

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图 1 反向传播神经网络模型预测结果

FEV1/FVC：1秒通气率；FVE1：1秒用力呼气量；FVC：用力肺活量；TC：总胆固醇；HDL-C：高密度脂蛋白胆固醇；TG：三酰甘油。

Figure 1. Prediction results of Back Propagation neural network model

FEV1/FVC: forceful expiratory volume in 1 second./forceful vital capacity; FVE1: forced expiratory volume in 1 second; FVC: forced vital capacity; TC: total Cholesterol; HDL-C: high-density lipoprotein cholesterol; TG: triglyceride.

下载: 全尺寸图片幻灯片

图 2 Logistic回归预测模型的混淆矩阵

Figure 2. Confusion matrix of the logistic regression model

下载: 全尺寸图片幻灯片

图 3 2个模型预测效果的ROC曲线

ROC: 受试者工作特征；BP：反向传播。

Figure 3. ROC curves of the predictive effects of the two models

ROC: receiver operating characteristic; BP: back propagation.

下载: 全尺寸图片幻灯片

表 1 研究对象基本特征

Table 1. Basic information of input variables

变量 Variable	病例组 Case group ^① (n=553)	对照组 Control group ^① (n=430)	χ²/Z值 value	P值 value
年龄组/岁 Age group/years	51.03±7.01	41.13±9.32	266.136	＜0.001
＜45	76(13.74)	273(63.49)
45~＜60	437(79.03)	153(35.58)
≥60	40(7.23)	4(0.93)
BMI/(kg·m^-2)			52.864	＜0.001
偏瘦 Thin (＜18.5)	9(1.63)	4(0.93)
正常 Normal (18.5~＜24.0)	229(41.41)	130(30.23)
超重 Overweight (24.0~＜28.0)	279(50.45)	202(46.98)
肥胖 Fat (≥28.0)	36(6.51)	94(21.86)
吸烟 Smoking			42.914	＜0.001
是 Yes	236(42.68)	274(63.72)
否 No	317(57.32)	156(36.28)
饮酒 Drinking			179.934	＜0.001
是 Yes	100(18.08)	256(59.53)
否 No	453(81.92)	174(40.47)
高血压 Hypertension			7.581	0.007
是 Yes	154(27.85)	87(20.23)
否 No	399(72.15)	343(79.77)
血脂四项 Four aspects of blood lipids/(mmol·L^-1)
TC	4.63(4.12, 4.96)	4.63(4.09, 5.20)	1.113	0.266
TG	1.77(1.28, 2.03)	1.53(1.02, 2.25)	-2.658	0.086
LDL-C	2.89(2.55, 3.16)	2.89(2.49, 3.28)	0.040	0.968
HDL-C	1.28(1.12, 1.39)	1.19(1.04, 1.33)	-4.274	＜0.05
肺功能检查 Pulmonary function test /%
FVC	90.00(73.05, 106.20)	85.64(80.26, 94.30)	-2.104	0.035
FEV1	93.00(77.15, 112.00)	90.97(83.68, 98.70)	-1.908	0.056
FEV1/FVC	87.00(78.80, 89.98)	105.67(94.17, 111.82)	16.958	＜0.001
职业暴露情况 Occupational exposure situation
工种 Type of work			68.472	＜0.001
掘进工 Heading man	96(17.36)	84(19.53)
采煤工 Coal miner	187(33.81)	124(28.84)
支护工 Support worker	70(12.66)	34(7.91)
混合工 Mixed worker	152(27.49)	72(16.74)
辅助工 Auxiliary worker	48(8.68)	116(26.98)
工龄/年 Seniority/year	21.57±9.22	10.19±8.02	278.241	＜0.001
＜10	70(12.66)	255(59.30)
10~＜20	178(32.19)	118(27.44)
20~＜30	173(31.28)	40(9.30)
≥30	132(23.87)	17(3.95)
注：TC，总胆固醇；TG，三酰甘油；LDL-C，低密度脂蛋白胆固醇；HDL-C，高密度脂蛋白胆固醇；FVC，用力肺活量；FEV1，1秒钟用力呼气量; FEV1/FVC, 1秒通气率。 ①以$\overline x \pm s$、M(P₂₅, P₇₅)或人数(占比/%)表示。 Note: TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FVC, forceful vital capacity; FEV1, forceful expiratory volume in 1 second; FEV1/FVC：forceful expiratory volume in 1 second/forceful vital capacity; FEV1/FVC, forceful expiratory volume in 1 second/forceful vital capacity. ① $\overline x \pm s$, M(P₂₅, P₇₅) or number of people (proportion/%).

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表 2 前10位特征变量重要性

Table 2. Importance of top 10 characteristic variables

排序 Sort	变量 Variable	重要性 Importance
1	FEV1/FVC/%	0.257
2	工龄/年 Seniority/year	0.189
3	工种 Type of work	0.069
4	年龄/岁 Age/years	0.066
5	FEV1/%	0.065
6	FVC/%	0.050
7	TC/(mmol·L^-1)	0.043
8	HDL-C/(mmol·L^-1)	0.043
9	TG/(mmol·L^-1)	0.043
10	饮酒 Drinking	0.041
注：FEV1/FVC, 1秒通气率；FEV1，1秒钟用力呼气量；FVC，用力肺活量；TC，总胆固醇；HDL-C，高密度脂蛋白胆固醇；TG，三酰甘油。 Note: FEV1/FVC, forceful expiratory volume in 1 second/forceful vital capacity; FEV1, forceful expiratory volume in 1 second; FVC, forceful vital capacity; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides.

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表 3 前10位特征变量重要性

Table 3. Importance of top 10 characteristic variables

变量 Variable	系数 Coefficient	回归标准差 ${s_{\overline x }}$	标准化系数 Standardized Coefficient	P值 value	共线性统计
变量 Variable	系数 Coefficient	回归标准差 ${s_{\overline x }}$	标准化系数 Standardized Coefficient	P值 value	容差 Tolerance	VIF
常量 Constant	1.421	0.152		0
FVC/%	0.000	0.000	0.048	0.292	0.249	4.019
FEV1/%	-0.001	0.000	-0.100	0.029	0.250	4.003
FEV1/FVC /%	0.010	0.001	0.303	＜0.001	0.742	1.347
饮酒 Drinking	-0.188	0.027	-0.182	＜0.001	0.789	1.267
TC/(mmol·L^-1)	0.024	0.013	0.045	0.060	0.900	1.111
TG/(mmol·L^-1)	-0.014	0.010	-0.033	0.166	0.917	1.090
HDL-C/(mmol·L^-1)	-0.072	0.045	-0.038	0.108	0.942	1.062
辅助工 Auxiliary worker	0.023	0.008	0.065	0.005	0.961	1.040
工龄/年 Seniority/year	-0.013	0.001	-0.270	＜0.001	0.606	1.651
年龄/岁 Age/years	-0.009	0.002	-0.171	＜0.001	0.588	1.700
注：FFVC，用力肺活量；FEV1，1秒钟用力呼气量；EV1/FVC：1秒通气率; TC，总胆固醇；TG，三酰甘油；HDL-C，高密度脂蛋白胆固醇; VIF，方差膨胀因子；FEV1/FVC：1秒通气率。 Note: FVC, forceful vital capacity; FEV1, forceful expiratory volume in 1 second; FEV1/FVC：forceful expiratory volume in 1 second/forceful vital capacity; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; VIF, variance inflation factor; FEV1/FVC：forceful expiratory volume in 1 second./forceful vital capacity.

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基于反向传播神经网络构建煤工尘肺的患病风险预测模型——一项以医院为基础的病例对照研究

doi: 10.16462/j.cnki.zhjbkz.2024.08.015

通讯作者:
罗铭忠，E-mail: lmz7344@163.com

雷立健，E-mail: wwdlijian@sxmu.edu.cn

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A neural network risk prediction model of coal workers′ pneumoconiosis-a hospital-based case-control study

Corresponding author: LEI Lijian, E-mail: wwdlijian@sxmu.edu.cn; LUO Mingzhong, E-mail: lmz7344@163.com<

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基于反向传播神经网络构建煤工尘肺的患病风险预测模型——一项以医院为基础的病例对照研究

doi: 10.16462/j.cnki.zhjbkz.2024.08.015

通讯作者: 罗铭忠，E-mail: lmz7344@163.com 雷立健，E-mail: wwdlijian@sxmu.edu.cn

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A neural network risk prediction model of coal workers′ pneumoconiosis-a hospital-based case-control study

Corresponding author: LEI Lijian, E-mail: wwdlijian@sxmu.edu.cn; LUO Mingzhong, E-mail: lmz7344@163.com<

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出版历程

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通讯作者:
罗铭忠，E-mail: lmz7344@163.com

雷立健，E-mail: wwdlijian@sxmu.edu.cn