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Text data extraction for a prospective, research-focused data mart: implementation and validation

Overview of attention for article published in BMC Medical Informatics and Decision Making, September 2012
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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 (80th percentile)

Mentioned by

twitter
10 tweeters

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
68 Mendeley
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Title
Text data extraction for a prospective, research-focused data mart: implementation and validation
Published in
BMC Medical Informatics and Decision Making, September 2012
DOI 10.1186/1472-6947-12-106
Pubmed ID
Authors

Monique Hinchcliff, Eric Just, Sofia Podlusky, John Varga, Rowland W Chang, Warren A Kibbe

Abstract

Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of 'machine generated' sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
United States 2 3%
Canada 1 1%
Unknown 63 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 21%
Student > Ph. D. Student 9 13%
Student > Master 9 13%
Student > Bachelor 6 9%
Student > Postgraduate 5 7%
Other 12 18%
Unknown 13 19%
Readers by discipline Count As %
Medicine and Dentistry 17 25%
Computer Science 13 19%
Engineering 5 7%
Agricultural and Biological Sciences 4 6%
Social Sciences 4 6%
Other 8 12%
Unknown 17 25%

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 15 October 2012.
All research outputs
#4,286,230
of 21,338,376 outputs
Outputs from BMC Medical Informatics and Decision Making
#413
of 1,861 outputs
Outputs of similar age
#29,255
of 148,991 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 21,338,376 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 1,861 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 well, scoring higher than 77% 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 148,991 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 80% 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