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Mining FDA drug labels for medical conditions

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2013
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Mentioned by

twitter
5 tweeters

Citations

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19 Dimensions

Readers on

mendeley
113 Mendeley
citeulike
1 CiteULike
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Title
Mining FDA drug labels for medical conditions
Published in
BMC Medical Informatics and Decision Making, April 2013
DOI 10.1186/1472-6947-13-53
Pubmed ID
Authors

Qi Li, Louise Deleger, Todd Lingren, Haijun Zhai, Megan Kaiser, Laura Stoutenborough, Anil G Jegga, Kevin Bretonnel Cohen, Imre Solti

Abstract

Cincinnati Children's Hospital Medical Center (CCHMC) has built the initial Natural Language Processing (NLP) component to extract medications with their corresponding medical conditions (Indications, Contraindications, Overdosage, and Adverse Reactions) as triples of medication-related information ([(1) drug name]-[(2) medical condition]-[(3) LOINC section header]) for an intelligent database system, in order to improve patient safety and the quality of health care. The Food and Drug Administration's (FDA) drug labels are used to demonstrate the feasibility of building the triples as an intelligent database system task.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 5%
Switzerland 1 <1%
Germany 1 <1%
Australia 1 <1%
Netherlands 1 <1%
Unknown 103 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 21%
Student > Master 16 14%
Student > Ph. D. Student 13 12%
Student > Doctoral Student 11 10%
Student > Postgraduate 7 6%
Other 20 18%
Unknown 22 19%
Readers by discipline Count As %
Computer Science 27 24%
Medicine and Dentistry 21 19%
Agricultural and Biological Sciences 8 7%
Psychology 6 5%
Biochemistry, Genetics and Molecular Biology 4 4%
Other 19 17%
Unknown 28 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 23 September 2013.
All research outputs
#12,390,469
of 21,346,377 outputs
Outputs from BMC Medical Informatics and Decision Making
#914
of 1,862 outputs
Outputs of similar age
#88,557
of 173,894 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#2
of 4 outputs
Altmetric has tracked 21,346,377 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,862 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 49th percentile – i.e., 49% 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 173,894 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% 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. This one has scored higher than 2 of them.