Journal
JOURNAL OF ATTENTION DISORDERS
Volume 27, Issue 3, Pages 324-331Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/10870547221136228
Keywords
ADHD screening; ADHD comorbidity; adult ADHD; machine-learning; mental health
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Screening for adult ADHD and differentiating it from comorbid mental health disorders are still clinically challenging. This pilot study showed that a novel screening method using composite scoring can improve detection accuracy of ADHD and inform the risk of ADHD with comorbidity.
Screening for adult Attention-Deficit/Hyperactivity Disorder (ADHD) and differentiating ADHD from comorbid mental health disorders remains to be clinically challenging. A screening tool for ADHD and comorbid mental health disorders is essential, as most adult ADHD is comorbid with several mental health disorders. The current pilot study enrolled 955 consecutive patients attending a tertiary mental health center in Canada and who completed EarlyDetect assessment, with 45.2% of patients diagnosed with ADHD. The best ADHD classification model using composite scoring achieved a balanced accuracy of 0.788, showing a 2.1% increase compared to standalone ADHD screening, detecting four more patients with ADHD per 100 patients. The classification model including ADHD with comorbidity was also successful (balanced accuracy = 0.712). The results suggest the novel screening method can improve ADHD detection accuracy and inform the risk of ADHD with comorbidity, and may further inform specific comorbidity including MDD and BD.
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