Data-Driven Diagnostics and the Potential of Mobile Artificial Intelligence for Digital Therapeutic Phenotyping in Computational Psychiatry.

Journal: Biological psychiatry. Cognitive neuroscience and neuroimaging
Published Date: Dec 13, 2019

Abstract

Data science and digital technologies have the potential to transform diagnostic classification. Digital technologies enable the collection of big data, and advances in machine learning and artificial intelligence enable scalable, rapid, and automated classification of medical conditions. In this review, we summarize and categorize various data-driven methods for diagnostic classification. In particular, we focus on autism as an example of a challenging disorder due to its highly heterogeneous nature. We begin by describing the frontier of data science methods for the neuropsychiatry of autism. We discuss early signs of autism as defined by existing pen-and-paper-based diagnostic instruments and describe data-driven feature selection techniques for determining the behaviors that are most salient for distinguishing children with autism from neurologically typical children. We then describe data-driven detection techniques, particularly computer vision and eye tracking, that provide a means of quantifying behavioral differences between cases and controls. We also describe methods of preserving the privacy of collected videos and prior efforts of incorporating humans in the diagnostic loop. Finally, we summarize existing digital therapeutic interventions that allow for data capture and longitudinal outcome tracking as the diagnosis moves along a positive trajectory. Digital phenotyping of autism is paving the way for quantitative psychiatry more broadly and will set the stage for more scalable, accessible, and precise diagnostic techniques in the field.

Authors

  • Peter Washington
    Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
  • Natalie Park
    Department of Biological Sciences, Columbia University, New York, New York.
  • Parishkrita Srivastava
    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California.
  • Catalin Voss
    Department of Computer Science, Stanford University, Stanford, California.
  • Aaron Kline
    Department of Pediatrics (Systems Medicine), Stanford University, Stanford, California; Department of Biomedical Data Science, Stanford University, Stanford, California.
  • Maya Varma
    Department of Computer Science, Stanford University, Stanford, California.
  • Qandeel Tariq
    Department of Pediatrics (Systems Medicine), Stanford University, Stanford, California; Department of Biomedical Data Science, Stanford University, Stanford, California.
  • Haik Kalantarian
    Department of Pediatrics (Systems Medicine), Stanford University, Stanford, California; Department of Biomedical Data Science, Stanford University, Stanford, California.
  • Jessey Schwartz
    Department of Pediatrics (Systems Medicine), Stanford University, Stanford, California; Department of Biomedical Data Science, Stanford University, Stanford, California.
  • Ritik Patnaik
    Department of Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts.
  • Brianna Chrisman
    Department of Bioengineering, Stanford University, Stanford, California.
  • Nathaniel Stockham
    Department of Neuroscience, Stanford University, Stanford, California.
  • Kelley Paskov
    Department of Biomedical Data Science, Stanford University, Stanford, California.
  • 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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