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Data-driven approach for creating synthetic electronic medical records

Overview of attention for article published in BMC Medical Informatics and Decision Making, October 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

twitter
4 tweeters
patent
1 patent
facebook
1 Facebook page

Citations

dimensions_citation
44 Dimensions

Readers on

mendeley
130 Mendeley
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Title
Data-driven approach for creating synthetic electronic medical records
Published in
BMC Medical Informatics and Decision Making, October 2010
DOI 10.1186/1472-6947-10-59
Pubmed ID
Authors

Anna L Buczak, Steven Babin, Linda Moniz

Abstract

New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 130 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
Netherlands 1 <1%
Germany 1 <1%
Malta 1 <1%
Canada 1 <1%
Unknown 121 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 22%
Student > Master 23 18%
Student > Ph. D. Student 21 16%
Other 7 5%
Student > Bachelor 7 5%
Other 29 22%
Unknown 14 11%
Readers by discipline Count As %
Computer Science 41 32%
Medicine and Dentistry 36 28%
Social Sciences 5 4%
Unspecified 5 4%
Engineering 5 4%
Other 23 18%
Unknown 15 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 02 July 2018.
All research outputs
#4,649,825
of 22,747,498 outputs
Outputs from BMC Medical Informatics and Decision Making
#428
of 1,985 outputs
Outputs of similar age
#20,507
of 98,939 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#5
of 18 outputs
Altmetric has tracked 22,747,498 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 78% 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 98,939 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 18 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 72% of its contemporaries.