Development and validation of an interpretable machine learning model for predicting low muscle mass in patients with rheumatoid arthritis: a multicenter study
Based on a large, multicenter dataset, we developed and validated an explainable ML model capable of identifying individuals with a high risk of low muscle mass among patients with rheumatoid arthritis. This model may serve as a decision-support tool for clinicians in guiding further screening and diagnosis of sarcopenia.
Authors
- Xiaojing Hao
- Xuanhe Tian
- Ting Liu (ORCID: https://orcid.org/0000-0003-1816-5551)
- Xiaohu Zhao (ORCID: https://orcid.org/0000-0001-7768-391X)
- Yuan Qu (ORCID: https://orcid.org/0000-0001-7498-133X)
- Yuan Liu (ORCID: https://orcid.org/0009-0007-0993-4458)
- Fei Zhou (ORCID: https://orcid.org/0000-0002-6197-3629)
- Ping Jiang (ORCID: https://orcid.org/0000-0002-3758-130X)
- Shuai Zhong (ORCID: https://orcid.org/0009-0004-9453-4130)
- Bin Zhou (ORCID: https://orcid.org/0000-0002-0950-9298)
Institutions
- Shandong University of Traditional Chinese Medicine (CN)
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-11-19
- DOI
- https://doi.org/10.3389/fmed.2025.1694320
- Primary Topic
- Nutrition and Health in Aging
- Type
- article
- Field-Weighted Citation Impact
- 0.00