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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Awarded by European Commission
Funding Acknowledgements
We gratefully acknowledge the support from the UK Engineering and Physical Sciences Research Council (EPSRC), grant number EP/L000296/1. The ADDUCE project from which this piece of research borrowed clinical data is funded by the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement number 260576.