Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Ni L

PROVIDER: S-EPMC9655771 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

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

Ni Linghao L Chen Fengqiong F Ran Ruihong R Li Xiaoping X Jin Nan N Zhang Huadong H Peng Bin B

International journal of environmental research and public health 20221101 21

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 neur ...[more]

PMID: 36361178

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Project description:Today, many companies develop modular systems to realise considerable product differentiation and variation while simultaneously reducing costs through economies of scale [1]. Designing these modular systems to be lasting and robust in a highly dynamic environment [2], avoiding subsequent modification cost [3,4] and staying innovative over the product lifecycle is crucial for sustainable success in an increasingly competitive market [5]. Two closely related surveys were carried out on the initial situation described above. The first survey deals with the major conflict between planning reliability and flexibility with regard to future boundary conditions that the described challenges lead to. The data presented in this article on the first survey was collected from German companies, mainly from the automotive industry, developing and manufacturing complex products using modular systems. Other represented companies operate in the machine and plant construction industry. The survey comprises the answers of 39 participants, gathered via online questionnaire. The 17 questions of the survey are divided into two topics: characteristics of the participants, and the problem description of modular systems in highly dynamic environments and the management thereof. The second survey deals with the anticipation of innovations in the design of modular systems and the selection of the right choice from a variety of possible innovations to be considered. The data presented in this article on the second survey was collected from German automotive manufacturers, which develop and manufacture complex products using modular systems. The survey comprises the answers of 501 participants, gathered via online questionnaire. The 14 questions of the survey are divided into two topics: characteristics of the participants, and the problem description of modular systems and the management thereof. The data obtained allows the identification of existing deficits and dedicated research on solutions.

Dataset Information

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

Publications

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

Similar Datasets

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