Identifying clinical features associated with electroconvulsive therapy response in adolescents with major depressive disorder using machine learning (opens in new tab)

BackgroundElectroconvulsive therapy (ECT) is an effective treatment for adolescent major depressive disorder (MDD), but its efficacy varies. This study utilized machine learning (ML) to identify baseline clinical factors associated with poor ECT response.MethodsWe retrospectively enrolled 503 adolescent MDD patients. A poor response was defined as a <50% reduction on the Hamilton Depression Scale (HAMD-24). The optimal ML algorithm (Random Forest, RF) was selected from nine candidates and the...