Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence.

ABSTRACT: Three-quarters of lifetime mental illness occurs by the age of 24, but relatively little is known about how to robustly identify youth at risk to target intervention efforts known to improve outcomes. Barriers to knowledge have included obtaining robust predictions while simultaneously analyzing large numbers of different types of candidate predictors. In a new, large, transdiagnostic youth sample and multidomain high-dimension data, we used 160 candidate predictors encompassing neural, prenatal, developmental, physiologic, sociocultural, environmental, emotional and cognitive features and leveraged three different machine learning algorithms optimized with a novel artificial intelligence meta-learning technique to predict individual cases of anxiety, depression, attention deficit, disrupt

SUBMITTER: de Lacy N 

PROVIDER: S-EPMC10564881 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature