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Non-redundant association rules between diseases and medications: an automated method for knowledge base construction

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

patent
1 patent

Readers on

mendeley
63 Mendeley
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Title
Non-redundant association rules between diseases and medications: an automated method for knowledge base construction
Published in
BMC Medical Informatics and Decision Making, April 2015
DOI 10.1186/s12911-015-0151-9
Pubmed ID
Authors

François Séverac, Erik A Sauleau, Nicolas Meyer, Hassina Lefèvre, Gabriel Nisand, Nicolas Jay

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 2%
Argentina 1 2%
Canada 1 2%
Unknown 60 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 22%
Student > Ph. D. Student 12 19%
Student > Master 7 11%
Student > Postgraduate 5 8%
Lecturer 3 5%
Other 8 13%
Unknown 14 22%
Readers by discipline Count As %
Computer Science 19 30%
Medicine and Dentistry 11 17%
Agricultural and Biological Sciences 4 6%
Mathematics 2 3%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 7 11%
Unknown 18 29%
Attention Score in Context

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 13 May 2020.
All research outputs
#7,645,563
of 23,278,709 outputs
Outputs from BMC Medical Informatics and Decision Making
#787
of 2,023 outputs
Outputs of similar age
#91,505
of 265,072 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#15
of 36 outputs
Altmetric has tracked 23,278,709 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,023 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 58% 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 265,072 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 55% of its contemporaries.
We're also able to compare this research output to 36 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 58% of its contemporaries.