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.