Accessibility settings

This paper is in the following e-collection/theme issue:

Machine Learning (3321) Respiratory Medicine (224) Occupational Health and Ergonomics/Prevention at the Workplace (399) Pulmonology, COPD (238) Public Health Data Analytics (138) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (175)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/80156, first published .
Construction worker in hard hat and safety vest holding smartphone in industrial setting

Research on the Prediction of Coal Workers’ Pneumoconiosis Based on Easily Detectable Clinical Data: Machine Learning Model Development and Validation Study

Research on the Prediction of Coal Workers’ Pneumoconiosis Based on Easily Detectable Clinical Data: Machine Learning Model Development and Validation Study

Authors of this article:

Haiquan Li1, 2 Author Orcid Image ;   Jiaqi Jia3 Author Orcid Image ;   Xu Shi2 Author Orcid Image ;   Yudie Dong3 Author Orcid Image ;   Songquan Wang3 Author Orcid Image ;   Yuming Cui3 Author Orcid Image ;   Wenlu Hang2 Author Orcid Image ;   Dekun Zhang1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Lee C, Park S, Hwang J, Woo S, Park Y, Seo J, Lee S, Ahn J, Yon D, Rhee S. Multioutput Machine Learning Model for Predicting Postoperative Outcomes After Liposuction: Algorithm Development and Validation Study in a Multicenter Cohort. JMIR Medical Informatics 2026;14:e82145 View