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Mining FDA drug labels using an unsupervised learning technique - topic modeling

Overview of attention for article published in BMC Bioinformatics, October 2011
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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)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

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1 X user
patent
2 patents

Citations

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89 Dimensions

Readers on

mendeley
136 Mendeley
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1 CiteULike
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Title
Mining FDA drug labels using an unsupervised learning technique - topic modeling
Published in
BMC Bioinformatics, October 2011
DOI 10.1186/1471-2105-12-s10-s11
Pubmed ID
Authors

Halil Bisgin, Zhichao Liu, Hong Fang, Xiaowei Xu, Weida Tong

Abstract

The Food and Drug Administration (FDA) approved drug labels contain a broad array of information, ranging from adverse drug reactions (ADRs) to drug efficacy, risk-benefit consideration, and more. However, the labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the labeling text in a consistent and accurate fashion for comparative analysis across drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Spain 1 <1%
Malaysia 1 <1%
Unknown 130 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 19%
Researcher 26 19%
Student > Master 16 12%
Student > Doctoral Student 10 7%
Student > Bachelor 8 6%
Other 32 24%
Unknown 18 13%
Readers by discipline Count As %
Computer Science 38 28%
Medicine and Dentistry 13 10%
Agricultural and Biological Sciences 10 7%
Engineering 9 7%
Social Sciences 7 5%
Other 37 27%
Unknown 22 16%
Attention Score in Context

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 19 July 2022.
All research outputs
#5,055,527
of 24,578,676 outputs
Outputs from BMC Bioinformatics
#1,792
of 7,556 outputs
Outputs of similar age
#27,492
of 143,105 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 102 outputs
Altmetric has tracked 24,578,676 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 7,556 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 75% 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 143,105 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 102 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.