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Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection

Overview of attention for article published in BMC Medical Informatics and Decision Making, August 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

Mentioned by

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2 X users
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1 patent

Citations

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67 Dimensions

Readers on

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174 Mendeley
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Title
Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection
Published in
BMC Medical Informatics and Decision Making, August 2014
DOI 10.1186/1472-6947-14-75
Pubmed ID
Authors

Nan Liu, Zhi Xiong Koh, Junyang Goh, Zhiping Lin, Benjamin Haaland, Boon Ping Ting, Marcus Eng Hock Ong

Abstract

The key aim of triage in chest pain patients is to identify those with high risk of adverse cardiac events as they require intensive monitoring and early intervention. In this study, we aim to discover the most relevant variables for risk prediction of major adverse cardiac events (MACE) using clinical signs and heart rate variability.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 174 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Austria 1 <1%
Brazil 1 <1%
Unknown 171 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 18%
Student > Ph. D. Student 22 13%
Student > Master 15 9%
Student > Bachelor 15 9%
Other 12 7%
Other 37 21%
Unknown 42 24%
Readers by discipline Count As %
Medicine and Dentistry 54 31%
Computer Science 19 11%
Nursing and Health Professions 17 10%
Engineering 12 7%
Agricultural and Biological Sciences 5 3%
Other 20 11%
Unknown 47 27%
Attention Score in Context

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 09 September 2016.
All research outputs
#6,779,244
of 22,761,738 outputs
Outputs from BMC Medical Informatics and Decision Making
#654
of 1,984 outputs
Outputs of similar age
#66,105
of 235,668 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#10
of 31 outputs
Altmetric has tracked 22,761,738 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 1,984 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 66% 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 235,668 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 71% of its contemporaries.
We're also able to compare this research output to 31 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 67% of its contemporaries.