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Markov logic networks for adverse drug event extraction from text

Overview of attention for article published in Knowledge & Information Systems, August 2016
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  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

twitter
3 tweeters

Citations

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6 Dimensions

Readers on

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38 Mendeley
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Title
Markov logic networks for adverse drug event extraction from text
Published in
Knowledge & Information Systems, August 2016
DOI 10.1007/s10115-016-0980-6
Pubmed ID
Authors

Sriraam Natarajan, Vishal Bangera, Tushar Khot, Jose Picado, Anurag Wazalwar, Vitor Santos Costa, David Page, Michael Caldwell

Abstract

Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring, and comparing the ability of these various approaches to accurately discover ADEs. This work is motivated by the observation that text sources such as the Medline/Medinfo library provide a wealth of information on human health. Unfortunately, ADEs often result from unexpected interactions, and the connection between conditions and drugs is not explicit in these sources. Thus, in this work we address the question of whether we can quantitatively estimate relationships between drugs and conditions from the medical literature. This paper proposes and studies a state-of-the-art NLP-based extraction of ADEs from text.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 21%
Other 8 21%
Unspecified 5 13%
Student > Ph. D. Student 5 13%
Student > Doctoral Student 4 11%
Other 8 21%
Readers by discipline Count As %
Computer Science 17 45%
Medicine and Dentistry 5 13%
Unspecified 5 13%
Agricultural and Biological Sciences 2 5%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Other 7 18%

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 11 November 2017.
All research outputs
#7,341,443
of 12,228,878 outputs
Outputs from Knowledge & Information Systems
#128
of 282 outputs
Outputs of similar age
#171,836
of 333,624 outputs
Outputs of similar age from Knowledge & Information Systems
#4
of 13 outputs
Altmetric has tracked 12,228,878 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 282 research outputs from this source. They receive a mean Attention Score of 1.9. This one has gotten more attention than average, scoring higher than 53% 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 333,624 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.