Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism.

Journal: Molecular autism
Published Date: Dec 19, 2017

Abstract

BACKGROUND: Autism spectrum disorder (ASD) diagnosis can be delayed due in part to the time required for administration of standard exams, such as the Autism Diagnostic Observation Schedule (ADOS). Shorter and potentially mobilized approaches would help to alleviate bottlenecks in the healthcare system. Previous work using machine learning suggested that a subset of the behaviors measured by ADOS can achieve clinically acceptable levels of accuracy. Here we expand on this initial work to build sparse models that have higher potential to generalize to the clinical population.

Authors

  • Sebastien Levy
    Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, CA USA.
  • Marlena Duda
    Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, CA USA.
  • Nick Haber
    School of Education, Stanford University, Stanford, California.
  • Dennis P Wall
    Department of Pediatrics (Systems Medicine), Stanford University, Stanford, California; Department of Biomedical Data Science, Stanford University, Stanford, California; Department of Psychiatry and Behavioral Sciences (by courtesy), Stanford University, Stanford, California. Electronic address: dpwall@stanford.edu.

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