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Annotation Analysis for Testing Drug Safety Signals using Unstructured Clinical Notes

Overview of attention for article published in Journal of Biomedical Semantics, April 2012
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Mentioned by

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1 tweeter

Citations

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69 Dimensions

Readers on

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127 Mendeley
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Title
Annotation Analysis for Testing Drug Safety Signals using Unstructured Clinical Notes
Published in
Journal of Biomedical Semantics, April 2012
DOI 10.1186/2041-1480-3-s1-s5
Pubmed ID
Authors

Paea LePendu, Srinivasan V Iyer, Cédrick Fairon, Nigam H Shah

Abstract

The electronic surveillance for adverse drug events is largely based upon the analysis of coded data from reporting systems. Yet, the vast majority of electronic health data lies embedded within the free text of clinical notes and is not gathered into centralized repositories. With the increasing access to large volumes of electronic medical data-in particular the clinical notes-it may be possible to computationally encode and to test drug safety signals in an active manner.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 127 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 5%
United Kingdom 2 2%
Finland 1 <1%
Brazil 1 <1%
Taiwan 1 <1%
Indonesia 1 <1%
Unknown 115 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 28%
Researcher 28 22%
Student > Master 11 9%
Other 8 6%
Student > Postgraduate 8 6%
Other 23 18%
Unknown 14 11%
Readers by discipline Count As %
Computer Science 38 30%
Medicine and Dentistry 31 24%
Agricultural and Biological Sciences 16 13%
Nursing and Health Professions 4 3%
Engineering 4 3%
Other 13 10%
Unknown 21 17%

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 March 2013.
All research outputs
#8,790,813
of 14,013,582 outputs
Outputs from Journal of Biomedical Semantics
#203
of 331 outputs
Outputs of similar age
#68,461
of 122,979 outputs
Outputs of similar age from Journal of Biomedical Semantics
#3
of 4 outputs
Altmetric has tracked 14,013,582 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 331 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 22nd percentile – i.e., 22% 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 122,979 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.