Primary predictors of adolescent anxiety and depression using machine learning

Abstract

Mood disorders are increasingly prevalent among adolescents and thought to signiticantly attect lite trajectories. The Adolescent Brain Cognitive Development Study is the largest longitudinal and multimodal dataset in the United States. Its purpose is to find risk factors for substance abuse. These are the initial findings obtained from analyzing the Adolescent Brain Cognitive Development Study dataset with machine learning (ML) to quantitatively assess depression and anxiety onset in adolescence. The results of this study will bring important insights into identifying the main risk factors for adverse health outcomes in addition to protective measures, allowing us to make predictions and develop early diagnostic strategies and personalized treatment to avoid long-term or permanent health consequences.

Margot Wagner

Postdoctoral Researcher

Interested in the use of data science and AI in mental health and using neuroscience to inspire next generation AI tools.