↓ Skip to main content

COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda

Overview of attention for article published in Respiratory Medicine, July 2020
Altmetric Badge

Mentioned by

twitter
1 X user

Readers on

mendeley
129 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.
Article details
Title
COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda
Published in
Respiratory Medicine, July 2020
DOI 10.1016/j.rmed.2020.106093
Pubmed ID
Authors

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
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 demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 129 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 13 10%
Researcher 12 9%
Student > Master 11 9%
Student > Bachelor 10 8%
Student > Doctoral Student 9 7%
Other 16 12%
Unknown 58 45%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 27 21%
Computer Science 17 13%
Nursing and Health Professions 12 9%
Biochemistry, Genetics and Molecular Biology 4 3%
Engineering 3 2%
Other 7 5%
Unknown 59 46%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 31 July 2020.
All research outputs
#20,943,365
of 30,025,272 outputs
Outputs from Respiratory Medicine
#3,520
of 4,291 outputs
Outputs of similar age
#283,322
of 433,687 outputs
Outputs of similar age from Respiratory Medicine
#32
of 44 outputs
Altmetric has tracked 30,025,272 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,291 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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 433,687 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.