Researchers at Rensselaer Polytechnic Institute who developed a blood test to help diagnose autism spectrum disorder have now successfully applied their distinctive big data-based approach to evaluating possible treatments.

Currently, diagnosis and evaluating the success of an intervention rely heavily on observations by professionals and caretakers.

"Having some kind of a measure that measures something that's happening inside the body is really important," said Juergen Hahn, systems biologist, professor, and head of the Rensselaer Department of Biomedical Engineering.

Hahn and his team use machine-learning algorithms to analyze complex data sets.

That is how he previously discovered patterns with certain metabolites in the blood of children with autism that can be used to successfully predict diagnosis.

In this most recent analysis, the team used a similar set of measurements from three different clinical trials that examined potential metabolic interventions.

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