A multi-biomarker machine learning approach for early prediction of interstitial lung disease in rheumatoid arthritis
Machine learning approaches, particularly XGBoost, demonstrate promising potential for early RA-ILD prediction. The integration of KL-6 and other identified biomarkers into clinical screening protocols may facilitate early detection and improved patient outcomes. These findings suggest that machine learning models could serve as valuable tools for risk stratification and early intervention in RA-ILD management, providing new approaches for individualized risk assessment in clinical practice.
Authors
- Neville Gai (ORCID: https://orcid.org/0000-0001-5932-2029)
- Yunqi Bao (ORCID: https://orcid.org/0000-0002-9871-679X)
- Wei Zhang (ORCID: https://orcid.org/0000-0001-9004-1161)
- Jiaojiao Xu (ORCID: https://orcid.org/0000-0001-5436-3059)
- Weili Bai
- Jing Li (ORCID: https://orcid.org/0000-0001-6463-6356)
Institutions
- Shanxi Medical University (CN)
- Shanxi Provincial People’s Hospital (CN)
Publication Details
- Journal
- BMC Pulmonary Medicine
- Published
- 2025-08-14
- DOI
- https://doi.org/10.1186/s12890-025-03855-y
- Citations
- 3
- Primary Topic
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Type
- article
- Field-Weighted Citation Impact
- 4.56
Funders
- Xi'an Municipal Bureau of Science and Technology