Journal of Autism and Developmental Disorders

, Volume 45, Issue 5, pp 1121–1136 | Cite as

Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises

  • Daniel BoneEmail author
  • Matthew S. Goodwin
  • Matthew P. Black
  • Chi-Chun Lee
  • Kartik Audhkhasi
  • Shrikanth Narayanan
Original Paper


Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead to misinformed conclusions. To illustrate this concern, the current paper critically evaluates and attempts to reproduce results from two studies (Wall et al. in Transl Psychiatry 2(4):e100, 2012a; PloS One 7(8), 2012b) that claim to drastically reduce time to diagnose autism using machine learning. Our failure to generate comparable findings to those reported by Wall and colleagues using larger and more balanced data underscores several conceptual and methodological problems associated with these studies. We conclude with proposed best-practices when using machine learning in autism research, and highlight some especially promising areas for collaborative work at the intersection of computational and behavioral science.


Autism diagnostic observation schedule Autism diagnostic interview Machine learning Signal processing Autism Diagnosis 



This work was supported by funds from NSF Award 1029035, “Computational Behavioral Science: Modeling, Analysis, and Visualization of Social and Communicative Behavior”, NIH grants P50 DC013027 and R01 DC012774, and the Alfred E. Mann Innovation in Engineering Fellowship. The authors are grateful to Shanping Qiu for her efforts in acquiring and preparing the BID data for analysis.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Daniel Bone
    • 1
    Email author
  • Matthew S. Goodwin
    • 2
  • Matthew P. Black
    • 3
  • Chi-Chun Lee
    • 4
  • Kartik Audhkhasi
    • 1
  • Shrikanth Narayanan
    • 1
  1. 1.Signal Analysis & Interpretation Laboratory (SAIL)University of Southern CaliforniaLos AngelesUSA
  2. 2.Department of Health SciencesNortheastern UniversityBostonUSA
  3. 3.Information Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA
  4. 4.Department of Electrical EngineeringNational Tsing Hua UniversityHsinchuTaiwan

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