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Opportunities and Challenges for Machine Learning in Rare Diseases

Overview of attention for article published in Frontiers in Medicine, October 2021
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Mentioned by

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2 X users

Citations

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41 Dimensions

Readers on

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52 Mendeley
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Title
Opportunities and Challenges for Machine Learning in Rare Diseases
Published in
Frontiers in Medicine, October 2021
DOI 10.3389/fmed.2021.747612
Pubmed ID
Authors

Sergio Decherchi, Elena Pedrini, Marina Mordenti, Andrea Cavalli, Luca Sangiorgi

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 19%
Other 4 8%
Student > Master 4 8%
Student > Ph. D. Student 4 8%
Student > Postgraduate 2 4%
Other 6 12%
Unknown 22 42%
Readers by discipline Count As %
Medicine and Dentistry 9 17%
Biochemistry, Genetics and Molecular Biology 6 12%
Computer Science 3 6%
Social Sciences 3 6%
Engineering 2 4%
Other 5 10%
Unknown 24 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 05 April 2022.
All research outputs
#19,823,618
of 25,233,554 outputs
Outputs from Frontiers in Medicine
#4,580
of 7,074 outputs
Outputs of similar age
#303,722
of 428,891 outputs
Outputs of similar age from Frontiers in Medicine
#340
of 490 outputs
Altmetric has tracked 25,233,554 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,074 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.7. This one is in the 30th percentile – i.e., 30% 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 428,891 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 490 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.