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

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. 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

Anxiety · Deep learning · Generalizability theory · Mental health · Psychosocial · Replication (statistics · Set (abstract data type · Child and Adolescent Psychosocial and Emotional Development · Digital Mental Health Interventions · Mental Health Research Topics

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