Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space Versus Learning in the Data Space

Attention-Deficit Hyperactive Disorder (ADHD) is one of the most common mental health disorders amongst school-aged children with an estimated prevalence of 5% in the global population (1). Stimulants, particularly methylphenidate (MPH), are the first-line option in the treatment of ADHD (2, 3) and are prescribed to an increasing number of children and adolescents in the US and the UK every year (4, 5), though recent studies suggest that this is tailing off, e.g. Holden et al. (6). Around 70% medication (7, 8, 9, 10). However, it is unclear which patient characteristics may moderate treatment effectiveness. As such, most existing research has focused on investigating univariate or multivariate correlations between a set of patient characteristics and the treatment outcome, with respect to dosage of one or several types of medication. The results of such studies are often contradictory and inconclusive due to a combination of small sample sizes, low-quality data or a lack of available information on covariates.