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Predicting opioid dependence from electronic health records with machine learning

Overview of attention for article published in BioData Mining, January 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)

Mentioned by

twitter
13 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
73 Mendeley
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Title
Predicting opioid dependence from electronic health records with machine learning
Published in
BioData Mining, January 2019
DOI 10.1186/s13040-019-0193-0
Authors

Randall J. Ellis, Zichen Wang, Nicholas Genes, Avi Ma’ayan

Twitter Demographics

The data shown below were collected from the profiles of 13 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 16%
Researcher 12 16%
Student > Bachelor 7 10%
Student > Master 7 10%
Student > Postgraduate 5 7%
Other 14 19%
Unknown 16 22%
Readers by discipline Count As %
Medicine and Dentistry 16 22%
Computer Science 11 15%
Nursing and Health Professions 4 5%
Social Sciences 4 5%
Psychology 4 5%
Other 12 16%
Unknown 22 30%

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 24 March 2019.
All research outputs
#2,831,676
of 15,329,228 outputs
Outputs from BioData Mining
#93
of 246 outputs
Outputs of similar age
#85,283
of 333,429 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 15,329,228 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 246 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. This one has gotten more attention than average, scoring higher than 62% 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 333,429 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 74% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them