Machine learning puts new lens on autism screening and diagnostics

Approximately one in 68 people are on the autism spectrum. Experts are unanimous on this: early intervention is critical for improving communication skills and addressing behavioral issues. But how can researchers expedite the identification of children in need of help and simultaneously provide a more clear-cut map for intervention and support? Researchers from the USC Signal Analysis and Interpretation Laboratory (SAIL) at the USC Viterbi School of Engineering Ming Hsieh’s Department of Electrical Engineering, along with autism research leaders Catherine Lord (of Weill Cornell Medical College) and Somer Bishop (of University of California, San Francisco), are now exploring whether machine learning might play an important role in helping screen for autism and guide caregiver and practitioner intervention. Their newest interdisciplinary collaboration and research is documented in the paper “Use of…


Link to Full Article: Machine learning puts new lens on autism screening and diagnostics