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Evaluating current automatic de-identification methods with Veteran’s health administration clinical documents

Overview of attention for article published in BMC Medical Research Methodology, July 2012
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Title
Evaluating current automatic de-identification methods with Veteran’s health administration clinical documents
Published in
BMC Medical Research Methodology, July 2012
DOI 10.1186/1471-2288-12-109
Pubmed ID
Authors

Oscar Ferrández, Brett R South, Shuying Shen, F Jeffrey Friedlin, Matthew H Samore, Stéphane M Meystre

Abstract

The increased use and adoption of Electronic Health Records (EHR) causes a tremendous growth in digital information useful for clinicians, researchers and many other operational purposes. However, this information is rich in Protected Health Information (PHI), which severely restricts its access and possible uses. A number of investigators have developed methods for automatically de-identifying EHR documents by removing PHI, as specified in the Health Insurance Portability and Accountability Act "Safe Harbor" method.This study focuses on the evaluation of existing automated text de-identification methods and tools, as applied to Veterans Health Administration (VHA) clinical documents, to assess which methods perform better with each category of PHI found in our clinical notes; and when new methods are needed to improve performance.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
United States 1 1%
Germany 1 1%
Canada 1 1%
Unknown 86 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 23%
Student > Ph. D. Student 15 16%
Student > Master 13 14%
Other 8 9%
Professor > Associate Professor 5 5%
Other 11 12%
Unknown 18 20%
Readers by discipline Count As %
Medicine and Dentistry 28 31%
Computer Science 21 23%
Social Sciences 4 4%
Engineering 4 4%
Linguistics 3 3%
Other 9 10%
Unknown 22 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 05 August 2012.
All research outputs
#18,312,024
of 22,673,450 outputs
Outputs from BMC Medical Research Methodology
#1,726
of 2,000 outputs
Outputs of similar age
#126,119
of 164,673 outputs
Outputs of similar age from BMC Medical Research Methodology
#29
of 34 outputs
Altmetric has tracked 22,673,450 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,000 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
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 164,673 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.