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Detecting Paroxysmal Coughing from Pertussis Cases Using Voice Recognition Technology

Overview of attention for article published in PLOS ONE, December 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

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6 X users
facebook
1 Facebook page

Readers on

mendeley
51 Mendeley
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1 CiteULike
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Article details
Title
Detecting Paroxysmal Coughing from Pertussis Cases Using Voice Recognition Technology
Published in
PLOS ONE, December 2013
DOI 10.1371/journal.pone.0082971
Pubmed ID
Authors
Abstract

Pertussis is highly contagious; thus, prompt identification of cases is essential to control outbreaks. Clinicians experienced with the disease can easily identify classic cases, where patients have bursts of rapid coughing followed by gasps, and a characteristic whooping sound. However, many clinicians have never seen a case, and thus may miss initial cases during an outbreak. The purpose of this project was to use voice-recognition software to distinguish pertussis coughs from croup and other coughs.

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Timeline Attention over time Attention Score history
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Activity
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X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 2%
Belgium 1 2%
Unknown 49 96%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 9 18%
Student > Master 6 12%
Other 4 8%
Student > Doctoral Student 4 8%
Student > Bachelor 4 8%
Other 10 20%
Unknown 14 27%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 10 20%
Computer Science 7 14%
Engineering 5 10%
Immunology and Microbiology 3 6%
Mathematics 1 2%
Other 6 12%
Unknown 19 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 26 August 2014.
All research outputs
#10,658,642
of 34,360,889 outputs
Outputs from PLOS ONE
#91,178
of 224,520 outputs
Outputs of similar age
#103,908
of 372,495 outputs
Outputs of similar age from PLOS ONE
#2,449
of 5,909 outputs
Altmetric has tracked 34,360,889 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 224,520 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.2. This one has gotten more attention than average, scoring higher than 59% 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 372,495 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 72% of its contemporaries.
We're also able to compare this research output to 5,909 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 58% of its contemporaries.