Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data.

ABSTRACT:

Background

Children with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.

Methods

Psychiatric and somatic diagnoses, family history of these disorders, measures of socioeconomic distress, and information about birth complications were obtained from the national registers in Sweden for 19,787 children with ADHD born between 1989 and 1993. We trained (a) a cross-sectional random forest (RF) model using data available by age 17 to predict SUD diagnosis between ages 18 and 19; and (b) a longitudinal recurrent neural network (RNN) model with the Long Short-Term Memory (LSTM) architecture to predict new diagnoses at each age.

Re

SUBMITTER: Zhang-James Y 

PROVIDER: S-EPMC7754321 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature