Computational Models for Biomedical Reasoning and Problem Solving
ADHD is being recognized as a diagnosis that persists into adulthood impacting educational and economic outcomes. There is an increased need to accurately diagnose this population through the development of reliable and valid outcome measures reflecting core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity (WMC) when compared to their peers. A reduction in WMC indicates attention control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as machine learning, to generate a relationship between ADHD and measures of WMC would be useful to advancing our understanding and treatment of ADHD in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a WMC 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.
Original Publication Citation
Michalek, A. M. P., Jayawardena, G., & Jayarathna, S. (2019). Predicting ADHD using eye gaze metrics indexing working memory capacity. In Computational Models for Biomedical Reasoning and Problem Solving (pp. 66-88). Hershey, PA: IGI Global.
Michalek, Anne M.P.; Jayawardena, Gavindya; and Jayarathna, Sampath, "Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity" (2019). Communication Disorders & Special Education Faculty Publications. 55.