ArticleFrontiers in public health2024
Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Global research landscape versus disease burden in pneumoconiosis diagnosis: a dual-source bibliometric analysis [1999-2025].Journal of thoracic disease · 2026Article
- Interpretable machine learning methods based on oscillometry and electric modeling for the diagnostic of respiratory dysfunction in silicosis.BMC medical informatics and decision making · 2026Article
- Multimodal Fusion of Chest X-Rays and Blood Biomarkers for Automated Silicosis Staging.Journal of clinical medicine · 2025Article
- Cytokine Profiles as Predictive Biomarkers of Disease Severity and Progression in Engineered Stone Silicosis: A Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning models for the prediction of preclinical coal workers' pneumoconiosis: integrating CT radiomics and occupational health surveillance records.Journal of translational medicine · 2025Article
- Development of a machine learning-based risk prediction model for early-stage pneumoconiosis: a retrospective study.Frontiers in medicine · 2025Article
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7 authors.
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Abstract
Objective: Due to the high global prevalence of silicosis and the ongoing challenges in its diagnosis, this pilot study aims to screen biomarkers from routine blood parameters and develop a multi-biomarker model for its early detection. Methods: A case-control study was conducted to screen biomarkers for the diagnosis of silicosis using LASSO regression, SVM and RF. A sample of 612 subjects (half cases and half controls) were randomly divided into training and test groups in a 2:1 ratio. Logistic regression analysis and receiver operating characteristic (ROC) curves were used to construct a multiple biomarker-based model for the diagnosis of silicosis, which was applied to both the training and the testing datasets. Results: The training cohort revealed significant statistical differences ( Conclusion: A diagnostic model based on DD, A/G, LDH and WBC is successfully proposed for silicosis diagnostics. It is cheap, sensitive, specific, and preliminarily offers a potential strategy for the large-scale screening of silicosis.
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