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

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.

Methods

Data stemmed from CoLaus|PsyCoLaus, a population-based cohort study. In total, 1350 participants, age 35-66 years without lifetime MDD at baseline participated in the physical and psychiatric baseline and at least one psychiatric follow-up evaluation. Models based on logistic regression, elastic net, random

SUBMITTER: Massell J 

PROVIDER: S-EPMC12449384 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature