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Rensselaer Polytechnic research shows big data approach can help evaluate autism treatments

Researchers at Rensselaer Polytechnic Institute have now successfully applied their distinctive big data-based approach to evaluating possible treatments for autism.
The findings, recently published in Frontiers in Cellular Neuroscience, have the potential to accelerate the development of successful medical interventions, according to a Rensselaer announcement.

One of the challenges in assessing the effectiveness of a treatment for autism is how to measure improvement. Currently, diagnosis and evaluating the success of an intervention rely heavily on observations by professionals and caretakers, according to the article.
 
“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 is a BMES member.
 
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. The researchers were able to compare data from before and after treatment, and look for correlations between those results and any observed changes of adaptive behavior.
 
“What we did here is showed that if you actively try to change concentrations of these metabolites that are being measured, then you will also see changes in the behavior,” Hahn said.

Read more HERE.