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<p>Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition that can negatively impact long-term outcomes for individuals. Early diagnosis is critical, yet demographic and clinical disparities can delay detection. Using electronic health records (EHRs) from a cohort of over 720,000 patients, we pretrained an EHR foundation model. We then fine tuned it to predict the likelihood of ADHD diagnosis and timing from birth until age 9 years in a pediatric cohort of over 140,000 patients. By age 5 years, the model achieved a time-dependent area under the receiver operating characteristic curve of 0.92 at a 4-year time horizon. Overall, the model maintained its performance across patients with differing demographics, including sex, race, ethnicity and insurance status. Our feature importance analysis found that ADHD was strongly associated with developmental, behavioral and psychiatric conditions. Our results suggest that EHR-based predictive models could help providers reliably identify children with ADHD in a timely manner.</p>

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Early attention deficit hyperactivity disorder prediction from longitudinal electronic health records

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • Elliot D. Hill,
  • De Rong Loh,
  • Naomi O. Davis,
  • Benjamin A. Goldstein,
  • Geraldine Dawson,
  • Matthew Engelhard

摘要

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition that can negatively impact long-term outcomes for individuals. Early diagnosis is critical, yet demographic and clinical disparities can delay detection. Using electronic health records (EHRs) from a cohort of over 720,000 patients, we pretrained an EHR foundation model. We then fine tuned it to predict the likelihood of ADHD diagnosis and timing from birth until age 9 years in a pediatric cohort of over 140,000 patients. By age 5 years, the model achieved a time-dependent area under the receiver operating characteristic curve of 0.92 at a 4-year time horizon. Overall, the model maintained its performance across patients with differing demographics, including sex, race, ethnicity and insurance status. Our feature importance analysis found that ADHD was strongly associated with developmental, behavioral and psychiatric conditions. Our results suggest that EHR-based predictive models could help providers reliably identify children with ADHD in a timely manner.