PUBLICATION DATE

2015-05

TITLE

Applying machine learning to facilitate autism diagnostics: pitfalls and promises.

ISSUE

5

VOLUME

45

ISSN (print)

N/A

ISSN (electronic)

N/A

ABSTRACT

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.

Related objects

JOURNAL BRAND

N/A (note: articles not published by Springer Nature have limited metadata)


FROM GRANT

  • Verbal/Non-Verbal Asynchrony In Adolescents With High-Functioning Autism
  • Minimally Verbal Asd: From Basic Mechanisms To Innovative Interventions
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    15 TRIPLES      14 PREDICATES      16 URIs      10 LITERALS

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    1 articles:3ee1af1658a879fda0bee160ce2e0281 sg:abstract 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.
    2 sg:doi 10.1007/s10803-014-2268-6
    3 sg:doiLink http://dx.doi.org/10.1007/s10803-014-2268-6
    4 sg:isFundedPublicationOf grants:117bc9903c0e3198005490bf34586366
    5 grants:7685d5cf11ee05d83f6168e447be1f00
    6 sg:issue 5
    7 sg:language English
    8 sg:license http://scigraph.springernature.com/explorer/license/
    9 sg:publicationYear 2015
    10 sg:publicationYearMonth 2015-05
    11 sg:scigraphId 3ee1af1658a879fda0bee160ce2e0281
    12 sg:title Applying machine learning to facilitate autism diagnostics: pitfalls and promises.
    13 sg:volume 45
    14 rdf:type sg:Article
    15 rdfs:label Article: Applying machine learning to facilitate autism diagnostics: pitfalls and promises.
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