Prospective prediction of first onset of major depressive disorder in midlife using machine learning.

Journal: Social psychiatry and psychiatric epidemiology
Published Date: Jun 18, 2025

Abstract

PURPOSE: In this paper we leverage machine learning (ML) models to prospectively predict the first onset of Major Depressive Disorder (MDD), one of the most common and disabling mental health conditions. While such prediction models hold potential for enabling early interventions, few studies have applied ML approaches to this task, and those that have are heterogeneous in nature. Moreover, the clinical utility of these predictive models remains largely unexamined.

Authors

  • Johannes Massell
    Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Missionsstrasse 62a, Basel, 4055, Switzerland.
  • Martin Preisig
    Psychiatric Epidemiology and Psychopathology Research Center, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Rte de Cery 25, 1008 Prilly, Switzerland.
  • Marcel Miché
    University of Basel, Department of Psychology, Division of Clinical Psychology and Epidemiology, Basel, Switzerland.
  • Marie-Pierre F Strippoli
    Psychiatric Epidemiology and Psychopathology Research Center, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Rte de Cery 25, 1008 Prilly, Switzerland.
  • Giorgio Pistis
    Psychiatric Epidemiology and Psychopathology Research Center, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Rte de Cery 25, 1008 Prilly, Switzerland.
  • Roselind Lieb
    University of Basel, Department of Psychology, Division of Clinical Psychology and Epidemiology, Basel, Switzerland. Electronic address: roselind.lieb@unibas.ch.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals