Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms.

ABSTRACT: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder in children and has a high chance of persisting in adulthood. The development of individualized, efficient, and reliable treatment strategies is limited by the lack of understanding of the underlying neural mechanisms. Diverging and inconsistent findings from existing studies suggest that ADHD may be simultaneously associated with multivariate factors across cognitive, genetic, and biological domains. Machine learning algorithms are more capable of detecting complex interactions between multiple variables than conventional statistical methods. Here we present a narrative review of the existing machine learning studies that have contributed to understanding mechanisms underlyi

SUBMITTER: Cao M 

PROVIDER: S-EPMC10313824 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature