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Deep Learning Model for Predicting Intradialytic Hypotension Without Privacy Infringement: A Retrospective Two-Center Study

Overview of attention for article published in Frontiers in Medicine, July 2022
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Title
Deep Learning Model for Predicting Intradialytic Hypotension Without Privacy Infringement: A Retrospective Two-Center Study
Published in
Frontiers in Medicine, July 2022
DOI 10.3389/fmed.2022.878858
Pubmed ID
Authors

Hyung Woo Kim, Seok-Jae Heo, Minseok Kim, Jakyung Lee, Keun Hyung Park, Gongmyung Lee, Song In Baeg, Young Eun Kwon, Hye Min Choi, Dong-Jin Oh, Chung-Mo Nam, Beom Seok Kim

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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.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 15%
Unspecified 1 8%
Student > Bachelor 1 8%
Student > Master 1 8%
Unknown 8 62%
Readers by discipline Count As %
Chemical Engineering 1 8%
Unspecified 1 8%
Nursing and Health Professions 1 8%
Computer Science 1 8%
Medicine and Dentistry 1 8%
Other 1 8%
Unknown 7 54%
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 25 June 2022.
All research outputs
#20,221,866
of 22,745,803 outputs
Outputs from Frontiers in Medicine
#4,812
of 5,572 outputs
Outputs of similar age
#346,807
of 434,112 outputs
Outputs of similar age from Frontiers in Medicine
#447
of 569 outputs
Altmetric has tracked 22,745,803 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,572 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.9. This one is in the 1st percentile – i.e., 1% 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 434,112 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 569 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.