An accurate predictive model for depressive symptoms in patients with primary Sjögren's disease based on machine learning algorithms
This study presents the first ML-based model for predicting depressive symptoms in pSjD patients, highlighting the significance of immuno-inflammatory and clinical factors in depression pathogenesis. The SVM model offers a robust, non-invasive tool for early identification of high-risk individuals, enabling timely and personalised interventions. However, this single-centre, retrospective design with a modest sample limits generalisability; therefore, independent multi-centre validation is required before clinical use.
Authors
- Jianjun Zou (ORCID: https://orcid.org/0000-0003-0886-3153)
- Kaizong Huang (ORCID: https://orcid.org/0000-0002-4926-6980)
- Wei Lu (ORCID: https://orcid.org/0000-0002-4150-8674)
- Yulan Tong (ORCID: https://orcid.org/0009-0006-0982-2803)
- Dinglei Su (ORCID: https://orcid.org/0000-0002-8573-3275)
- Yuqing Yan (ORCID: https://orcid.org/0009-0002-4436-0545)
- Xiaoqin Wang (ORCID: https://orcid.org/0000-0001-5890-8079)
- Leilei Yang
- Yuzhan Jin
Institutions
- Nanjing Medical University (CN)
Publication Details
- Journal
- Clinical and Experimental Rheumatology
- Published
- 2025-08-14
- DOI
- https://doi.org/10.55563/clinexprheumatol/ar9d4k
- Primary Topic
- Salivary Gland Disorders and Functions
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China