Early identification of children with Attention-Deficit/Hyperactivity Disorder (ADHD)
Signs and symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) are present at preschool ages and often not identified for early intervention. We aimed to use machine learning to detect ADHD early among kindergarten-aged children using population-level administrative health data and a childhood developmental vulnerability surveillance tool: Early Development Instrument (EDI). The study cohort consists of 23,494 children born in Alberta, Canada, who attended kindergarten in 2016 without a diagnosis of ADHD. In a four-year follow-up period, 1,680 children were later identified with ADHD using case definition. We trained and tested machine learning models to predict ADHD prospectively. The best-performing model using administrative and EDI data could reliably predict ADHD and achieved an Area Under the Curve (AUC) of 0.811 during cross-validation. Key predictive factors included EDI subdomain scores, sex, and socioeconomic status. Our findings suggest that machine learning algorithms that use population-level surveillance data could be a valuable tool for early identification of ADHD.
Authors
- Shelly Vik (ORCID: https://orcid.org/0000-0001-9977-8063)
- Ashley Gaskin (ORCID: https://orcid.org/0000-0001-9027-7254)
- Dan Metes
- Yipeng Song (ORCID: https://orcid.org/0000-0002-7180-2647)
- Magdalena Janus (ORCID: https://orcid.org/0000-0001-7806-1696)
- Russell Greiner (ORCID: https://orcid.org/0000-0001-8327-934X)
- Bo Cao (ORCID: https://orcid.org/0000-0001-9338-3271)
- Lawrence Kiyang
- Fernanda Talarico
- Jake Hayward (ORCID: https://orcid.org/0000-0003-4919-8946)
- Andrew J. Greenshaw (ORCID: https://orcid.org/0000-0002-9097-900X)
- Mengzhe Wang (ORCID: https://orcid.org/0000-0003-4654-6845)
- Yang S. Liu (ORCID: https://orcid.org/0000-0003-0406-8056)
- Yanbo Zhang (ORCID: https://orcid.org/0000-0002-2421-157X)
- Yifeng Wei (ORCID: https://orcid.org/0000-0001-9124-0381)
- Ghalib Ahmed
- D Wearmouth
- Alex Alexander
Institutions
- Alberta Health Services (CA)
- Ministry of Health (CA)
- University of Alberta (CA)
- Government of Alberta (CA)
- McMaster University (CA)
Publication Details
- Journal
- PLOS Digital Health
- Published
- 2024-11-07
- DOI
- https://doi.org/10.1371/journal.pdig.0000620
- Citations
- 7
- Primary Topic
- Attention Deficit Hyperactivity Disorder
- Type
- article
- Field-Weighted Citation Impact
- 2.70
Funders
- Brain and Behavior Research Foundation
- Schizophrenia Research Fund
- Government of Alberta
- Mental Health Foundation
- Canada Research Chairs
- Mitacs
- Alberta Innovates
- University Hospital Foundation