ArticleInternational journal of environmental research and public health2022
A Deep Learning-Based Model for Predicting Abnormal Liver Function in Workers in the Automotive Manufacturing Industry: A Cross-Sectional Survey in Chongqing, China.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 4 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.