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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 (63rd percentile)

Mentioned by

twitter
6 tweeters

Readers on

mendeley
11 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

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 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 27%
Student > Doctoral Student 2 18%
Student > Ph. D. Student 2 18%
Lecturer 1 9%
Researcher 1 9%
Other 1 9%
Unknown 1 9%
Readers by discipline Count As %
Medicine and Dentistry 3 27%
Computer Science 3 27%
Biochemistry, Genetics and Molecular Biology 2 18%
Business, Management and Accounting 1 9%
Nursing and Health Professions 1 9%
Other 0 0%
Unknown 1 9%

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
#3,856,345
of 13,725,722 outputs
Outputs from BMC Medical Informatics and Decision Making
#451
of 1,237 outputs
Outputs of similar age
#91,137
of 249,003 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 4 outputs
Altmetric has tracked 13,725,722 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,237 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 62% 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 249,003 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 63% of its contemporaries.
We're also able to compare this research output to 4 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