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Machine Learning-Based Extraction of Breast Cancer Receptor Status From Bilingual Free-Text Pathology Reports

Overview of attention for article published in Frontiers in Digital Health, August 2021
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (60th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 X users

Citations

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3 Dimensions

Readers on

mendeley
22 Mendeley
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Title
Machine Learning-Based Extraction of Breast Cancer Receptor Status From Bilingual Free-Text Pathology Reports
Published in
Frontiers in Digital Health, August 2021
DOI 10.3389/fdgth.2021.692077
Pubmed ID
Authors

Antoine Pironet, Hélène A. Poirel, Tim Tambuyzer, Harlinde De Schutter, Lien van Walle, Joris Mattheijssens, Kris Henau, Liesbet Van Eycken, Nancy Van Damme

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 9%
Student > Master 2 9%
Other 2 9%
Researcher 1 5%
Unknown 15 68%
Readers by discipline Count As %
Medicine and Dentistry 3 14%
Computer Science 2 9%
Nursing and Health Professions 1 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Linguistics 1 5%
Other 1 5%
Unknown 13 59%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 29 October 2021.
All research outputs
#13,030,117
of 23,310,485 outputs
Outputs from Frontiers in Digital Health
#298
of 579 outputs
Outputs of similar age
#170,381
of 432,529 outputs
Outputs of similar age from Frontiers in Digital Health
#37
of 65 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 579 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. This one is in the 48th percentile – i.e., 48% 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 432,529 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 60% of its contemporaries.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.