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Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2020
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Mentioned by

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3 X users

Citations

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24 Dimensions

Readers on

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53 Mendeley
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Title
Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches
Published in
BMC Medical Informatics and Decision Making, July 2020
DOI 10.1186/s12911-020-01185-z
Pubmed ID
Authors

Peng-Nien Yin, Kishan KC, Shishi Wei, Qi Yu, Rui Li, Anne R. Haake, Hiroshi Miyamoto, Feng Cui

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 13%
Researcher 6 11%
Student > Master 5 9%
Student > Doctoral Student 4 8%
Student > Bachelor 4 8%
Other 7 13%
Unknown 20 38%
Readers by discipline Count As %
Medicine and Dentistry 10 19%
Computer Science 8 15%
Engineering 4 8%
Biochemistry, Genetics and Molecular Biology 2 4%
Physics and Astronomy 2 4%
Other 3 6%
Unknown 24 45%
Attention Score in Context

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 19 July 2020.
All research outputs
#18,733,166
of 23,885,338 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,552
of 2,048 outputs
Outputs of similar age
#264,761
of 368,509 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#38
of 56 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,048 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 21st percentile – i.e., 21% 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 368,509 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.