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Knowledge-based extraction of adverse drug events from biomedical text

Overview of attention for article published in BMC Bioinformatics, March 2014
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  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
53 Dimensions

Readers on

mendeley
118 Mendeley
citeulike
4 CiteULike

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Title
Knowledge-based extraction of adverse drug events from biomedical text
Published in
BMC Bioinformatics, March 2014
DOI 10.1186/1471-2105-15-64
Pubmed ID
24593054
Authors

Ning Kang, Bharat Singh, Chinh Bui, Zubair Afzal, Erik M van Mulligen, Jan A Kors

Abstract

Many biomedical relation extraction systems are machine-learning based and have to be trained on large annotated corpora that are expensive and cumbersome to construct. We developed a knowledge-based relation extraction system that requires minimal training data, and applied the system for the extraction of adverse drug events from biomedical text. The system consists of a concept recognition module that identifies drugs and adverse effects in sentences, and a knowledge-base module that establishes whether a relation exists between the recognized concepts. The knowledge base was filled with information from the Unified Medical Language System. The performance of the system was evaluated on the ADE corpus, consisting of 1644 abstracts with manually annotated adverse drug events. Fifty abstracts were used for training, the remaining abstracts were used for testing.

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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 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 3 3%
France 2 2%
United States 2 2%
Australia 1 <1%
Portugal 1 <1%
Japan 1 <1%
Spain 1 <1%
Unknown 107 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 27%
Researcher 22 19%
Student > Master 14 12%
Student > Doctoral Student 9 8%
Student > Bachelor 7 6%
Other 21 18%
Unknown 13 11%
Readers by discipline Count As %
Computer Science 61 52%
Agricultural and Biological Sciences 10 8%
Medicine and Dentistry 8 7%
Engineering 5 4%
Social Sciences 3 3%
Other 14 12%
Unknown 17 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 July 2014.
All research outputs
#12,618,010
of 21,364,317 outputs
Outputs from BMC Bioinformatics
#4,017
of 6,930 outputs
Outputs of similar age
#97,672
of 201,831 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 5 outputs
Altmetric has tracked 21,364,317 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,930 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 38th percentile – i.e., 38% 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 201,831 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 5 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.

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