  ADHD is being recognized as a diagnosis which persists into adulthood
impacting economic, occupational, and educational outcomes. There is an
increased need to accurately diagnose and recommend interventions for this
population. One consideration is the development and implementation of reliable
and valid outcome measures which reflect core diagnostic criteria. For example,
adults with ADHD have reduced working memory capacity when compared to their
peers (Michalek et al., 2014). A reduction in working memory capacity indicates
attentional control deficits which align with many symptoms outlined on
behavioral checklists used to diagnose ADHD. Using computational methods, such
as eye tracking technology, to generate a relationship between ADHD and
measures of working memory capacity would be useful to advancing our
understanding and treatment of the diagnosis in adults. This chapter will
outline a feasibility study in which eye tracking was used to measure eye gaze
metrics during a working memory capacity task for adults with and without ADHD
and machine learning algorithms were applied to generate a feature set unique
to the ADHD diagnosis. The chapter will summarize the purpose, methods,
results, and impact of this study.
