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A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images

Overview of attention for article published in Frontiers in Neuroinformatics, July 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
7 X users
q&a
1 Q&A thread

Citations

dimensions_citation
62 Dimensions

Readers on

mendeley
146 Mendeley
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Title
A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images
Published in
Frontiers in Neuroinformatics, July 2019
DOI 10.3389/fninf.2019.00053
Pubmed ID
Authors

Sucheta Chauhan, Lovekesh Vig, Michele De Filippo De Grazia, Maurizio Corbetta, Shandar Ahmad, Marco Zorzi

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 146 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 146 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 12%
Student > Master 17 12%
Student > Bachelor 16 11%
Researcher 10 7%
Student > Postgraduate 6 4%
Other 25 17%
Unknown 54 37%
Readers by discipline Count As %
Computer Science 26 18%
Engineering 18 12%
Medicine and Dentistry 9 6%
Neuroscience 7 5%
Earth and Planetary Sciences 4 3%
Other 17 12%
Unknown 65 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 17 May 2020.
All research outputs
#4,785,735
of 23,885,338 outputs
Outputs from Frontiers in Neuroinformatics
#243
of 783 outputs
Outputs of similar age
#91,519
of 348,687 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#4
of 15 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 783 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has gotten more attention than average, scoring higher than 68% 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 348,687 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.