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Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm

Overview of attention for article published in BMC Medical Informatics and Decision Making, August 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

blogs
1 blog
twitter
14 tweeters
patent
2 patents
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
32 Dimensions

Readers on

mendeley
96 Mendeley
citeulike
1 CiteULike
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Title
Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm
Published in
BMC Medical Informatics and Decision Making, August 2013
DOI 10.1186/1472-6947-13-81
Pubmed ID
Authors

Anil N Makam, Oanh K Nguyen, Billy Moore, Ying Ma, Ruben Amarasingham

Abstract

Effective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 3 3%
United Kingdom 2 2%
Austria 1 1%
Ghana 1 1%
Australia 1 1%
Ireland 1 1%
Switzerland 1 1%
United States 1 1%
Unknown 85 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 23%
Researcher 18 19%
Student > Master 17 18%
Other 7 7%
Student > Postgraduate 6 6%
Other 20 21%
Unknown 6 6%
Readers by discipline Count As %
Medicine and Dentistry 28 29%
Computer Science 13 14%
Engineering 7 7%
Social Sciences 6 6%
Psychology 5 5%
Other 22 23%
Unknown 15 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 06 November 2018.
All research outputs
#873,105
of 14,787,109 outputs
Outputs from BMC Medical Informatics and Decision Making
#51
of 1,357 outputs
Outputs of similar age
#10,462
of 156,269 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 14,787,109 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,357 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 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 156,269 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 93% 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