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Automated de-identification of free-text medical records

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2008
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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 (87th percentile)

Mentioned by

twitter
3 tweeters
patent
2 patents
wikipedia
1 Wikipedia page

Citations

dimensions_citation
202 Dimensions

Readers on

mendeley
272 Mendeley
citeulike
7 CiteULike
connotea
2 Connotea
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Title
Automated de-identification of free-text medical records
Published in
BMC Medical Informatics and Decision Making, July 2008
DOI 10.1186/1472-6947-8-32
Pubmed ID
Authors

Ishna Neamatullah, Margaret M Douglass, Li-wei H Lehman, Andrew Reisner, Mauricio Villarroel, William J Long, Peter Szolovits, George B Moody, Roger G Mark, Gari D Clifford

Abstract

Text-based patient medical records are a vital resource in medical research. In order to preserve patient confidentiality, however, the U.S. Health Insurance Portability and Accountability Act (HIPAA) requires that protected health information (PHI) be removed from medical records before they can be disseminated. Manual de-identification of large medical record databases is prohibitively expensive, time-consuming and prone to error, necessitating automatic methods for large-scale, automated de-identification.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 272 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
United Kingdom 5 2%
France 2 <1%
Brazil 2 <1%
Australia 1 <1%
Ireland 1 <1%
Norway 1 <1%
Indonesia 1 <1%
China 1 <1%
Other 2 <1%
Unknown 249 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 61 22%
Student > Master 58 21%
Student > Ph. D. Student 54 20%
Other 15 6%
Student > Bachelor 14 5%
Other 48 18%
Unknown 22 8%
Readers by discipline Count As %
Computer Science 99 36%
Medicine and Dentistry 58 21%
Engineering 18 7%
Agricultural and Biological Sciences 11 4%
Arts and Humanities 10 4%
Other 45 17%
Unknown 31 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 16 April 2020.
All research outputs
#2,050,140
of 17,444,955 outputs
Outputs from BMC Medical Informatics and Decision Making
#173
of 1,583 outputs
Outputs of similar age
#20,398
of 166,315 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 17,444,955 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,583 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 89% 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 166,315 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 87% 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