Development and multi-center validation of a school-home integrated machine learning model for early screening of attention-deficit/hyperactivity disorder in school-aged children

Key Points

• ADHD screening accuracy increased, indicating a promising new tool for educators and parents.
• The model achieved a diagnostic performance metric of 90% in identifying ADHD cases across multiple centers.
• Observational analysis involved data from various schools, integrating home and school reports for comprehensive assessment.
• Integration of machine learning into routine screening may enhance early detection rates, highlighting a significant step in child mental health.