Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI

Abstract

AbstractInternalizing disorders (depression, anxiety, somatic symptom disorder) are among the most common mental health conditions that can substantially reduce daily life function. Early adolescence is an important developmental stage for the increase in prevalence of internalizing disorders and understanding specific factors that predict their onset may be germane to intervention and prevention strategies. We analyzed ∼6,000 candidate predictors from multiple knowledge domains (cognitive, psychosocial, neural, biological) contributed by children of late elementary school age (9-10 yrs) and their parents in the ABCD cohort to construct individual-level models predicting the later (11-12 yrs) onset of depression, anxiety and somatic symptom disorder using deep learning with artificial neural networks. Deep learning was guided by an evolutionary algorithm that jointly performed optimization across hyperparameters and automated feature selection, allowing more candidate predictors and a wider variety of predictor types to be analyzed than the largest previous comparable machine learning studies. We found that the future onset of internalizing disorders could be robustly predicted in early adolescence with AUROCs ≥∼0.90 and ≥∼80% accuracy. Each disorder had a specific set of predictors, though parent problem behavioral traits and sleep disturbances represented cross-cutting themes. Additional computational experiments revealed that psychosocial predictors were more important to predicting early adolescent internalizing disorders than cognitive, neural or biological factors and generated models with better performance. We also observed that the accuracy of individual-level models was highly correlated to the relative importance of their constituent predictors, suggesting that principled searches for predictors with higher importance or effect sizes could support the construction of more accurate individual-level models of internalizing disorders. Future work, including replication in additional datasets, will help test the generalizability of our findings and explore their application to other stages in human development and mental health conditions.