Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion.

Journal: Journal of child psychology and psychiatry, and allied disciplines
Published Date: Aug 1, 2016

Abstract

BACKGROUND: Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools.

Authors

  • Daniel Bone
    Signal Analysis & Interpretation Laboratory (SAIL), University of Southern California, 3710 McClintock Ave., Los Angeles, CA, 90089, USA, dbone@usc.edu.
  • Somer L Bishop
    San Francisco School of Medicine, University of California, San Francisco, CA, USA.
  • Matthew P Black
  • Matthew S Goodwin
    Department of Health Sciences, Northeastern University, Boston, MA, USA, m.goodwin@northeastern.edu.
  • Catherine Lord
    Center for Autism and the Developing Brain, Weill Cornell Medical College, New York, NY, USA.
  • Shrikanth S Narayanan

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