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EHR data is an incredibly rich source of information that can help predict diagnoses well before they usually happen. Credit: Neuroscience News<\/p>\nSummary: A new study demonstrates that artificial intelligence can accurately estimate a child\u2019s risk of developing ADHD years before a clinical diagnosis occurs. By mining \u201chidden patterns\u201d in routine Electronic Health Records (EHR) from birth through early childhood, the AI identifies combinations of developmental and behavioral markers that human clinicians might overlook during brief visits.<\/p>\n
This tool is designed to act as a \u201cclinical safety net,\u201d ensuring at-risk children receive early evaluations and support during critical developmental windows.<\/p>\n
Key Facts<\/p>\n
The Dataset: Researchers analyzed the medical history of over 140,000 children, creating a massive comparative baseline of those with and without ADHD.Early Detection: The AI model analyzes data from birth and becomes highly accurate at estimating future risk by age 5, well before the average age of diagnosis.Equitable Performance: A critical finding was the model\u2019s consistent accuracy across various demographics, including sex, race, ethnicity, and insurance status, suggesting it could help reduce existing disparities in ADHD care.Support, Not Diagnosis: The tool is explicitly not an \u201cAI doctor.\u201d Its purpose is to flag children who should be prioritized for screening by primary care providers or specialists.Better Outcomes: Early identification is directly linked to improved academic, social, and long-term health outcomes, as it allows for evidence-based interventions before a child falls behind.<\/p>\n
Source: Duke University<\/p>\n
Attention-deficit\/hyperactivity disorder (ADHD) affects millions of children, yet many go years without a diagnosis, missing the chance for early support that can change long-term outcomes even when early signs are present.<\/p>\n
In a new study, Duke Health researchers found that artificial intelligence tools can analyze routine electronic health records to accurately estimate a child\u2019s risk of developing ADHD years before a typical diagnosis. By reviewing patterns in everyday medical data, the approach could help flag children who may benefit from earlier evaluation and follow-up.<\/p>\n
EHR data is an incredibly rich source of information that can help predict diagnoses well before they usually happen. Credit: Neuroscience News<\/p>\n
The research, published in\u00a0Nature Mental Health\u00a0on April 27, highlights how powerful insights can come from information already collected during regular health care visits to help support early decision making by primary care providers.<\/p>\n
\u201cWe have this incredibly rich source of information sitting in electronic health records,\u201d said Elliot Hill, lead author of the study and data scientist in the\u00a0Department of Biostatistics & Bioinformatics\u00a0at\u00a0Duke University School of Medicine.<\/p>\n
\u201cThe idea was to see whether patterns hidden in that data could help us predict which children might later be diagnosed with ADHD, well before that diagnosis usually happens.\u201d<\/p>\n
To arrive at the findings, researchers analyzed electronic health records from more than 140,000 children, with and without ADHD. They trained a specialized AI model to look at medical history from birth through early childhood. The model learned to recognize combinations of developmental, behavioral, and clinical events that often appeared years before an ADHD diagnosis was made.<\/p>\n
The model was highly accurate at estimating future ADHD risk in children age 5 and older, with consistent performance across patient characteristics like sex, race, ethnicity, and insurance status.<\/p>\n
Importantly, the tool does not make a diagnosis. It identifies children who may benefit from closer attention by their pediatric primary care provider or an earlier referral for ADHD assessment by a specialist.<\/p>\n
\u201cThis is not an AI doctor,\u201d said\u00a0Matthew Engelhard, M.D., Ph.D., in Duke\u2019s Department of Biostatistics & Bioinformatics, and senior author of the study. \u201cIt\u2019s a tool to help clinicians focus their time and resources, so kids who need help don\u2019t fall through the cracks or wait years for answers.\u201d<\/p>\n
The researchers note that earlier identification for screening could lead to earlier diagnosis and therefore earlier support, which is linked to better academic, social, and health outcomes for children with ADHD. They also emphasize the need for further studies before such tools are used in clinical settings.<\/p>\n
\u201cChildren with ADHD can really struggle when their needs aren\u2019t understood and adequate supports are not in place,\u201d said study author,\u00a0Naomi Davis, Ph.D., associate professor in the\u00a0Department of Psychiatry and Behavioral Sciences. \u201cConnecting families with timely, evidence-based interventions is essential for helping them achieve their goals and laying a foundation for future success.\u201d<\/p>\n
Hill and Engelhard have also\u00a0researched\u00a0the use of AI models in predicting potential risks and causes for mental illness in adolescents.<\/p>\n
In addition to Hill Engelhard, and Davis, the authors for this study include De Rong Loh, Benjamin A. Goldstein, and Geraldine Dawson.<\/p>\n
Funding: The study was supported by grants from the National Institute of Mental Health (K01-MH127309, UL1 TR002553) and National Center for Advancing Translational Sciences.<\/p>\n
Key Questions Answered:Q: What kind of \u201chidden patterns\u201d is the AI looking for?<\/p>\n
A: The AI looks at the timing and combination of events, such as specific developmental delays, sleep disruptions, or frequent visits for behavioral concerns, that may seem insignificant on their own but together form a \u201crisk signature\u201d for ADHD.<\/p>\n
Q: Does this mean AI will start diagnosing my kids?<\/p>\n
A: No. Senior author Dr. Matthew Engelhard emphasizes that this is a resource-management tool. It helps pediatricians know which kids need a closer look so they don\u2019t \u201cfall through the cracks\u201d while waiting years for a standard evaluation.<\/p>\n
Q: Why do we need AI for this if we have doctors?<\/p>\n
A: Primary care providers often have very limited time with patients. AI can scan thousands of pages of a child\u2019s medical history in seconds, highlighting relevant clinical trends that might have happened years prior or with a different physician.<\/p>\n
Editorial Notes:This article was edited by a Neuroscience News editor.Journal paper reviewed in full.Additional context added by our staff.About this AI and ADHD research news<\/p>\n
Author:\u00a0Stephanie Lopez<\/a>
Source:\u00a0Duke University<\/a>
Contact:\u00a0Stephanie Lopez \u2013 Duke University
Image:\u00a0The image is credited to Neuroscience News<\/p>\n