Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children.

ABSTRACT: Oppositional defiant disorder and conduct disorder, collectively referred to as disruptive behavior disorders (DBDs), are prevalent psychiatric disorders in children. Early diagnosis of DBDs is crucial because they can increase the risks of other mental health and substance use disorders without appropriate psychosocial interventions and treatment. However, diagnosing DBDs is challenging as they are often comorbid with other disorders, such as attention-deficit/hyperactivity disorder, anxiety, and depression. In this study, a multimodal ensemble three-dimensional convolutional neural network (3D CNN) deep learning model was used to classify children with DBDs and typically developing children. The study participants included 419 females and 681 males, aged 108-131 months who were enrolled

SUBMITTER: Menon SS 

PROVIDER: S-EPMC8652047 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature