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The freetext matching algorithm: a computer program to extract diagnoses and causes of death from unstructured text in electronic health records

Overview of attention for article published in BMC Medical Informatics and Decision Making, August 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#47 of 1,363)
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

blogs
1 blog
twitter
21 tweeters

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
94 Mendeley
citeulike
3 CiteULike
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Title
The freetext matching algorithm: a computer program to extract diagnoses and causes of death from unstructured text in electronic health records
Published in
BMC Medical Informatics and Decision Making, August 2012
DOI 10.1186/1472-6947-12-88
Pubmed ID
Authors

Anoop D Shah, Carlos Martinez, Harry Hemingway

Abstract

Electronic health records are invaluable for medical research, but much information is stored as free text rather than in a coded form. For example, in the UK General Practice Research Database (GPRD), causes of death and test results are sometimes recorded only in free text. Free text can be difficult to use for research if it requires time-consuming manual review. Our aim was to develop an automated method for extracting coded information from free text in electronic patient records.

Twitter Demographics

The data shown below were collected from the profiles of 21 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 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 5 5%
United States 2 2%
Canada 2 2%
Unknown 85 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 33 35%
Student > Ph. D. Student 21 22%
Student > Master 11 12%
Other 9 10%
Student > Postgraduate 6 6%
Other 7 7%
Unknown 7 7%
Readers by discipline Count As %
Medicine and Dentistry 40 43%
Computer Science 17 18%
Agricultural and Biological Sciences 4 4%
Social Sciences 3 3%
Engineering 2 2%
Other 10 11%
Unknown 18 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 13 November 2019.
All research outputs
#833,919
of 15,010,975 outputs
Outputs from BMC Medical Informatics and Decision Making
#47
of 1,363 outputs
Outputs of similar age
#6,354
of 128,522 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 15,010,975 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 1,363 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 128,522 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 95% 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