By Dr. Sanchari Sinha Dutta, Ph.D.Jun 26 2023Reviewed by Lily Ramsey, LLM Scientists at the University of Michigan have developed machine learning models to predict childhood attention-deficit hyperactivity disorder symptoms from neurocognitive testing and child characteristics.Study: Generalizable prediction of childhood ADHD symptoms from neurocognitive testing and youth characteristics. Image Credit: Ahlapot/Shutterstock.
In the current study, scientists have developed and validated generalizable machine learning models to predict trait ADHD symptoms in independent data using neurocognitive testing and child characteristics. Two modeling strategies were used in the study. A comprehensive predictive modeling method was used to estimate the predictive value of individual characteristics regardless of their possible redundancy. In addition, a sparse predictive modeling method was used to develop more economical models.
For study sites left out of the model-fitting process, predictive models explained 15 – 20% and 12 – 13% of individual variations in ADHD symptoms at the one-year and two-year time points, respectively. These observations indicate that the models are highly generalizable to independent data.
Source: Education Headlines (educationheadlines.net)
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