"A Deep Learning-Based Model for Predicting Abnormal Liver Function in Workers in the Automotive Manufacturing Industry: A Cross-Sectional Survey in Chongqing, China"

作者全名："Ni, Linghao; Chen, Fengqiong; Ran, Ruihong; Li, Xiaoping; Jin, Nan; Zhang, Huadong; Peng, Bin"

作者地址："[Ni, Linghao; Peng, Bin] Chongqing Med Univ, Sch Publ Hlth, Chongqing 400016, Peoples R China; [Chen, Fengqiong; Ran, Ruihong; Li, Xiaoping; Jin, Nan; Zhang, Huadong] Chongqing Ctr Dis Control & Prevent, Dept Occupat Hlth & Radiat Hlth, Chongqing 400042, Peoples R China"

通信作者："Peng, B (通讯作者)，Chongqing Med Univ, Sch Publ Hlth, Chongqing 400016, Peoples R China.; Zhang, HD (通讯作者)，Chongqing Ctr Dis Control & Prevent, Dept Occupat Hlth & Radiat Hlth, Chongqing 400042, Peoples R China."

来源：INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH

ESI学科分类：ENVIRONMENT/ECOLOGY

WOS号：WOS:000883527700001

JCR分区：Q2

影响因子：4.614

年份：2022

卷号：19

期号：21

开始页：　

结束页：　

文献类型：Article

关键词：abnormal liver function; deep learning; automotive manufacturing industry; risk factors; predictive model

摘要："To identify the influencing factors and develop a predictive model for the risk of abnormal liver function in the automotive manufacturing industry works in Chongqing. Automotive manufacturing workers in Chongqing city surveyed during 2019-2021 were used as the study subjects. Logistic regression analysis was used to identify the influencing factors of abnormal liver function. A restricted cubic spline model was used to further explore the influence of the length of service. Finally, a deep neural network-based model for predicting the risk of abnormal liver function among workers was developed. Of all 6087 study subjects, a total of 1018 (16.7%) cases were detected with abnormal liver function. Increased BMI, length of service, DBP, SBP, and being male were independent risk factors for abnormal liver function. The risk of abnormal liver function rises sharply with increasing length of service below 10 years. AUC values of the model were 0.764 (95% CI: 0.746-0.783) and 0.756 (95% CI: 0.727-0.786) in the training and test sets, respectively. The other four evaluation indices of the DNN model also achieved good values."

基金机构：Chongqing Municipal Health Commission; Chongqing Science and Technology Bureau [2022ZDXM034]

基金资助正文：This research was funded by Chongqing Municipal Health Commission and Chongqing Science and Technology Bureau (No. 2022ZDXM034).