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Detecting Visually Observable Disease Symptoms from Faces

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, August 2016
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Mentioned by

twitter
1 tweeter

Citations

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6 Dimensions

Readers on

mendeley
39 Mendeley
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Title
Detecting Visually Observable Disease Symptoms from Faces
Published in
EURASIP Journal on Bioinformatics & Systems Biology, August 2016
DOI 10.1186/s13637-016-0048-7
Pubmed ID
Authors

Kuan Wang, Jiebo Luo

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 38 97%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 10 26%
Student > Ph. D. Student 8 21%
Researcher 7 18%
Student > Master 3 8%
Professor > Associate Professor 2 5%
Other 3 8%
Unknown 6 15%
Readers by discipline Count As %
Computer Science 11 28%
Engineering 9 23%
Medicine and Dentistry 5 13%
Nursing and Health Professions 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Other 3 8%
Unknown 6 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 April 2020.
All research outputs
#12,961,427
of 16,322,934 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#30
of 52 outputs
Outputs of similar age
#207,794
of 278,529 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#1
of 1 outputs
Altmetric has tracked 16,322,934 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 52 research outputs from this source. They receive a mean Attention Score of 2.0. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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 278,529 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
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