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Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises

Overview of attention for article published in Journal of Autism & Developmental Disorders, October 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

news
2 news outlets
blogs
2 blogs
twitter
14 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
43 Dimensions

Readers on

mendeley
153 Mendeley
Title
Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises
Published in
Journal of Autism & Developmental Disorders, October 2014
DOI 10.1007/s10803-014-2268-6
Pubmed ID
Authors

Daniel Bone, Matthew S. Goodwin, Matthew P. Black, Chi-Chun Lee, Kartik Audhkhasi, Shrikanth Narayanan

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.

Twitter Demographics

The data shown below were collected from the profiles of 14 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Canada 1 <1%
Israel 1 <1%
Mexico 1 <1%
Colombia 1 <1%
Unknown 146 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 25%
Researcher 30 20%
Student > Master 14 9%
Student > Bachelor 11 7%
Other 10 7%
Other 30 20%
Unknown 19 12%
Readers by discipline Count As %
Computer Science 28 18%
Psychology 25 16%
Medicine and Dentistry 15 10%
Engineering 15 10%
Neuroscience 9 6%
Other 27 18%
Unknown 34 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 39. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 July 2018.
All research outputs
#462,257
of 13,974,181 outputs
Outputs from Journal of Autism & Developmental Disorders
#193
of 3,414 outputs
Outputs of similar age
#8,392
of 212,767 outputs
Outputs of similar age from Journal of Autism & Developmental Disorders
#5
of 70 outputs
Altmetric has tracked 13,974,181 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,414 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 212,767 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 70 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.