Journal article

Personalized medication response prediction for attention-deficit hyperactivity disorder: Learning in the model space vs. learning in the data space

HK Wong, PA Tiffin, MJ Chappell, TE Nichols, PR Welsh, OM Doyle, BC Lopez-Kolkovska, SK Inglis, D Coghill, Y Shen, P Tiño

Frontiers in Physiology | FRONTIERS MEDIA SA | Published : 2017

Open access

Abstract

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 (American Psychiatric Association, 2013). Stimulants, particularly methylphenidate (MPH), are the first-line option in the treatment of ADHD (Reeves and Schweitzer, 2004; Dopheide and Pliszka, 2009) and are prescribed to an increasing number of children and adolescents in the US and the UK every year (Safer et al., 1996; McCarthy et al., 2009), though recent studies suggest that this is tailing off, e.g., Holden et al. (2013). Around 70% of children demonstrate a clinically significant treatment response to st..

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