Prediction of Attention-Deficit/Hyperactivity Disorder Diagnosis Using Brief, Low-Cost Clinical Measures: A Competitive Model Evaluation. (May 2023)

• Record Type: Journal Article
• Authors: Mooney, Michael A.; Neighbor, Christopher; Karalunas, Sarah; Dieckmann, Nathan F.; Nikolas, Molly; Nousen, Elizabeth; Tipsord, Jessica; Song, Xubo; Nigg, Joel T.
• Is Part Of: Clinical psychological science. Volume 11:Number 3(2023)
• Page Start: 458
• Page End: 475
• Publication Date: 2023-05
• Subjects: attention deficit hyperactivity disorder -- classification -- machine learning; Clinical psychology -- Periodicals; 616.89
• DOI: 10.1177/21677026221120236
• Languages: English
• ISSNs: 2167-7026

Abstract

Proper diagnosis of attention-deficit/hyperactivity disorder (ADHD) is costly, requiring in-depth evaluation via interview, multi-informant and observational assessment, and scrutiny of possible other conditions. The increasing availability of data may allow the development of machine-learning algorithms capable of accurate diagnostic predictions using low-cost measures to supplement human decision-making. We report on the performance of multiple classification methods used to predict a clinician-consensus ADHD diagnosis. Methods ranged from fairly simple (e.g., logistic regression) to more complex (e.g., random forest) but emphasized a multistage Bayesian approach. Classifiers were evaluated in two large ( N > 1, 000) independent cohorts. The multistage Bayesian classifier provided an intuitive approach consistent with clinical workflows and was able to predict expert consensus ADHD diagnosis with high accuracy (> 86%)—though not significantly better than other methods. Results suggest that parent and teacher surveys are sufficient for high-confidence classifications in the vast majority of cases, but an important minority require additional evaluation for accurate diagnosis.