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Machine learning to assist clinical decision-making during the COVID-19 pandemic

Overview of attention for article published in Bioelectronic Medicine, July 2020
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)

Mentioned by

news
19 news outlets
twitter
6 tweeters

Citations

dimensions_citation
48 Dimensions

Readers on

mendeley
154 Mendeley
Title
Machine learning to assist clinical decision-making during the COVID-19 pandemic
Published in
Bioelectronic Medicine, July 2020
DOI 10.1186/s42234-020-00050-8
Pubmed ID
Authors

Shubham Debnath, Douglas P. Barnaby, Kevin Coppa, Alexander Makhnevich, Eun Ji Kim, Saurav Chatterjee, Viktor Tóth, Todd J. Levy, Marc d. Paradis, Stuart L. Cohen, Jamie S. Hirsch, Theodoros P. Zanos, Lance B. Becker, Jennifer Cookingham, Karina W. Davidson, Andrew J. Dominello, Louise Falzon, Thomas McGinn, Jazmin N. Mogavero, Gabrielle A. Osorio

Twitter Demographics

The data shown below were collected from the profiles of 6 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 154 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 154 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 26 17%
Student > Ph. D. Student 25 16%
Student > Master 14 9%
Researcher 13 8%
Lecturer 8 5%
Other 32 21%
Unknown 36 23%
Readers by discipline Count As %
Computer Science 28 18%
Medicine and Dentistry 22 14%
Engineering 12 8%
Nursing and Health Professions 11 7%
Unspecified 7 5%
Other 24 16%
Unknown 50 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 144. 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 18 November 2020.
All research outputs
#185,181
of 19,441,086 outputs
Outputs from Bioelectronic Medicine
#3
of 78 outputs
Outputs of similar age
#6,714
of 300,586 outputs
Outputs of similar age from Bioelectronic Medicine
#1
of 1 outputs
Altmetric has tracked 19,441,086 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 78 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.5. This one has done particularly well, scoring higher than 96% 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 300,586 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% 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