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A clinical text classification paradigm using weak supervision and deep representation

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
43 Dimensions

Readers on

mendeley
45 Mendeley
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Title
A clinical text classification paradigm using weak supervision and deep representation
Published in
BMC Medical Informatics and Decision Making, January 2019
DOI 10.1186/s12911-018-0723-6
Pubmed ID
Authors

Yanshan Wang, Sunghwan Sohn, Sijia Liu, Feichen Shen, Liwei Wang, Elizabeth J. Atkinson, Shreyasee Amin, Hongfang Liu

Twitter Demographics

The data shown below were collected from the profiles of 5 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 18%
Student > Bachelor 6 13%
Student > Master 5 11%
Researcher 4 9%
Other 4 9%
Other 11 24%
Unknown 7 16%
Readers by discipline Count As %
Computer Science 13 29%
Medicine and Dentistry 7 16%
Engineering 6 13%
Biochemistry, Genetics and Molecular Biology 3 7%
Neuroscience 1 2%
Other 5 11%
Unknown 10 22%

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 16 November 2019.
All research outputs
#4,910,972
of 16,213,150 outputs
Outputs from BMC Medical Informatics and Decision Making
#552
of 1,477 outputs
Outputs of similar age
#143,361
of 389,041 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#36
of 84 outputs
Altmetric has tracked 16,213,150 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 1,477 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 62% 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 389,041 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 62% of its contemporaries.
We're also able to compare this research output to 84 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.