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What we learn from linking data

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The NIH has launched the new Autism Data Science Initiative: https://dpcpsi.nih.gov/autism-data-science-initiative/funding-opportunities#section1, which brings questions about why linking different data sets is important. It can be done without including personal identifying information, and it should be done following ethical guidelines. If done correctly, using large datasets can answer questions relating to treatment, cause, better identification and personalized medicine for those on the spectrum. So what has linking data done for families? This week’s podcast summarizes longitudinal research that follows individuals across time, linking their information across different ages to look at factors that predict outcomes, environmental factors, and how to best support those on the spectrum.

https://pubmed.ncbi.nlm.nih.gov/40420626

https://bmcpsychology.biomedcentral.com/articles/10.1186/s40359-025-02739-4

https://pubmed.ncbi.nlm.nih.gov/40391067

https://pubmed.ncbi.nlm.nih.gov/40309015

https://pubmed.ncbi.nlm.nih.gov/40401338

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How does autism prediction work?

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This podcast provides updates on studies that help with prediction of an autism diagnosis – which is important for preparing for the future and for intervening early. First, a study that uses environmental factors to create an equation for the probability of a diagnosis following a combination of of non-genetic factors only which does a fairly good, but not perfect, job at predicting a diagnosis. Second, a study that looks at the accuracy of a machine that predicts autism from eye gaze as early as 9 months of age and with only a 2 minute test. This one wasn’t as accurate as the one that takes longer and tests older kids, but it’s a first step. No ONE thing does a perfect job at predicting a diagnosis – it’s going to be a combination of things, tested over time and multiple times that will be most helpful at predicting a diagnosis. Both studies are open access!

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10904522/pdf/fpsyt-15-1291356.pdf

https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/38429348/