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Rapid identification of COVID-19 severity in CT scans through classification of deep features

Overview of attention for article published in BioMedical Engineering OnLine, August 2020
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)

Mentioned by

twitter
10 tweeters

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
131 Mendeley
Title
Rapid identification of COVID-19 severity in CT scans through classification of deep features
Published in
BioMedical Engineering OnLine, August 2020
DOI 10.1186/s12938-020-00807-x
Pubmed ID
Authors

Zekuan Yu, Xiaohu Li, Haitao Sun, Jian Wang, Tongtong Zhao, Hongyi Chen, Yichuan Ma, Shujin Zhu, Zongyu Xie

Twitter Demographics

The data shown below were collected from the profiles of 10 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 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 131 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 21 16%
Student > Master 16 12%
Unspecified 12 9%
Other 9 7%
Researcher 9 7%
Other 24 18%
Unknown 40 31%
Readers by discipline Count As %
Medicine and Dentistry 22 17%
Computer Science 15 11%
Unspecified 15 11%
Engineering 11 8%
Nursing and Health Professions 5 4%
Other 17 13%
Unknown 46 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 06 January 2021.
All research outputs
#5,967,752
of 19,898,358 outputs
Outputs from BioMedical Engineering OnLine
#150
of 766 outputs
Outputs of similar age
#118,902
of 309,843 outputs
Outputs of similar age from BioMedical Engineering OnLine
#1
of 1 outputs
Altmetric has tracked 19,898,358 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 766 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 80% 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 309,843 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 61% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them