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Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data.

Lucile Ter-MinassianNatalia VianiAlice WickershamLauren CrossRobert StewartSumithra VelupillaiJohnny M Downs
Published in: BMJ open (2022)
ML approaches using linked routinely collected education and health data offer accurate, low-cost and scalable prediction models of ADHD. These approaches could help identify areas of need and inform resource allocation. Introducing 'fairness weighting' attenuates some sociodemographic biases which would otherwise underestimate ADHD risk within minority groups.
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