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Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health records

Overview of attention for article published in Artificial Intelligence in Medicine, July 2020
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users

Readers on

mendeley
85 Mendeley
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Article details
Title
Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health records
Published in
Artificial Intelligence in Medicine, July 2020
DOI 10.1016/j.artmed.2020.101930
Pubmed ID
Authors

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Timeline Attention over time Attention Score history
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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 85 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 13 15%
Student > Ph. D. Student 9 11%
Lecturer 8 9%
Student > Bachelor 7 8%
Student > Master 6 7%
Other 11 13%
Unknown 31 36%
Readers by discipline
Readers by discipline Count As %
Computer Science 15 18%
Medicine and Dentistry 7 8%
Biochemistry, Genetics and Molecular Biology 5 6%
Engineering 5 6%
Business, Management and Accounting 3 4%
Other 14 16%
Unknown 36 42%
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 22 September 2022.
All research outputs
#18,946,280
of 27,448,938 outputs
Outputs from Artificial Intelligence in Medicine
#652
of 981 outputs
Outputs of similar age
#282,370
of 434,871 outputs
Outputs of similar age from Artificial Intelligence in Medicine
#17
of 27 outputs
Altmetric has tracked 27,448,938 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 981 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 24th percentile – i.e., 24% 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 434,871 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 27 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.