What is it about?
Many children exhibit symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) at a young age, but it is often diagnosed at a later stage. This delay in diagnosis can deprive children of the necessary support that they require. To address this issue, we conducted a study to develop a model that could predict ADHD in kindergarteners by analyzing various information readily available for this age group in 2016, including health records, demographics, and teacher-rated developmental assessments. We then followed these children for four years to evaluate the accuracy of our model in predicting their later ADHD diagnosis.
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Why is it important?
Why is it important? This research demonstrates the innovative use of machine learning in mental health and public health, showcasing how existing health data can be transformed into actionable insights for early detection of developmental disorders. By identifying children at risk of ADHD during their kindergarten years, we open new possibilities for timely intervention and support. This early detection approach not only has the potential to improve individual children's developmental trajectories but also represents a significant step forward in preventive mental health care. The study's findings could lead to future works that can inform policy decisions, enhance resource allocation in educational settings, and potentially enable families to access vital support services when they can make the most impact on their children's lives.
Perspectives
This research holds a special place in my heart as it represents the culmination of extraordinary teamwork between our academic team, government partners, and dedicated trainees. The enthusiasm and commitment from our funders and stakeholders have been remarkable. I am particularly proud of how we leveraged existing health data to create something meaningful – a tool that could potentially change the trajectory of young lives. What makes this work especially meaningful is knowing that our findings could lead to future works that help families who are struggling to understand their children's challenges. As a researcher, there's nothing more rewarding than seeing your work translate into real-world impact. I hope this model will serve as a stepping stone for earlier interventions, enabling children with ADHD to receive support when it matters most.
Bo Cao
University of Alberta
Read the Original
This page is a summary of: Early identification of children with Attention-Deficit/Hyperactivity Disorder (ADHD), PLOS Digital Health, November 2024, PLOS,
DOI: 10.1371/journal.pdig.0000620.
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