JAMA Psychiatry. 2026 Oct 21. doi: 10.1001/jamapsychiatry.2026.3232. Online ahead of print. ABSTRACT IMPORTANCE: Early identification of patients at high risk of relapse is key to personalized preventive strategies in major depressive disorder (MDD). However, current predictive models do not adequately account for the multimodal factors contributing to MDD relapse and fail to provide clinically meaningful predictions. OBJECTIVE: To predict relapse within 2 years in patients with MDD by using a comprehensive machine learning pipeline that integrates an unprecedented set of multimodal data. DESIGN, SETTING, AND PARTICIPANTS: This naturalistic longitudinal prognostic study used data from the Marburg-Münster Affective Disorders […]
