↓ Skip to main content

Predicting opioid dependence from electronic health records with machine learning

Overview of attention for article published in BioData Mining, January 2019
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#44 of 310)
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

news
1 news outlet
twitter
10 X users

Citations

dimensions_citation
66 Dimensions

Readers on

mendeley
152 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Predicting opioid dependence from electronic health records with machine learning
Published in
BioData Mining, January 2019
DOI 10.1186/s13040-019-0193-0
Pubmed ID
Authors

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

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users 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 152 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 152 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 15%
Researcher 18 12%
Student > Bachelor 16 11%
Student > Master 12 8%
Student > Doctoral Student 10 7%
Other 24 16%
Unknown 49 32%
Readers by discipline Count As %
Medicine and Dentistry 23 15%
Computer Science 20 13%
Nursing and Health Professions 8 5%
Social Sciences 8 5%
Psychology 7 5%
Other 31 20%
Unknown 55 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 14 April 2021.
All research outputs
#2,160,218
of 23,125,690 outputs
Outputs from BioData Mining
#44
of 310 outputs
Outputs of similar age
#53,660
of 438,408 outputs
Outputs of similar age from BioData Mining
#1
of 8 outputs
Altmetric has tracked 23,125,690 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 310 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done well, scoring higher than 85% 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 438,408 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 87% of its contemporaries.
We're also able to compare this research output to 8 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