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A clustering approach for detecting implausible observation values in electronic health records data

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 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
6 X users

Citations

dimensions_citation
34 Dimensions

Readers on

mendeley
70 Mendeley
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Title
A clustering approach for detecting implausible observation values in electronic health records data
Published in
BMC Medical Informatics and Decision Making, July 2019
DOI 10.1186/s12911-019-0852-6
Pubmed ID
Authors

Hossein Estiri, Jeffrey G. Klann, Shawn N. Murphy

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 readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 20%
Student > Master 8 11%
Student > Doctoral Student 6 9%
Researcher 5 7%
Unspecified 3 4%
Other 7 10%
Unknown 27 39%
Readers by discipline Count As %
Computer Science 15 21%
Medicine and Dentistry 5 7%
Nursing and Health Professions 5 7%
Unspecified 3 4%
Biochemistry, Genetics and Molecular Biology 3 4%
Other 10 14%
Unknown 29 41%
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 03 August 2019.
All research outputs
#7,345,736
of 23,881,329 outputs
Outputs from BMC Medical Informatics and Decision Making
#704
of 2,030 outputs
Outputs of similar age
#127,136
of 347,878 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#21
of 47 outputs
Altmetric has tracked 23,881,329 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 2,030 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 64% 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 347,878 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 47 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.