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Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population

Overview of attention for article published in Frontiers in Global Women's Health, July 2023
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
2 news outlets
twitter
3 X users

Readers on

mendeley
30 Mendeley
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Title
Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population
Published in
Frontiers in Global Women's Health, July 2023
DOI 10.3389/fgwh.2023.1161157
Pubmed ID
Authors

Santosh Yogendra Shah, Sumant Saxena, Satya Pavitra Rani, Naresh Nelaturi, Sheena Gill, Beth Tippett Barr, Joyce Were, Sammy Khagayi, Gregory Ouma, Victor Akelo, Errol R. Norwitz, Rama Ramakrishnan, Dickens Onyango, Manoj Teltumbade

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 13%
Student > Doctoral Student 3 10%
Professor 1 3%
Unspecified 1 3%
Lecturer 1 3%
Other 0 0%
Unknown 20 67%
Readers by discipline Count As %
Medicine and Dentistry 4 13%
Computer Science 2 7%
Nursing and Health Professions 2 7%
Arts and Humanities 1 3%
Social Sciences 1 3%
Other 1 3%
Unknown 19 63%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 17 April 2024.
All research outputs
#2,406,631
of 26,505,350 outputs
Outputs from Frontiers in Global Women's Health
#64
of 564 outputs
Outputs of similar age
#42,032
of 370,706 outputs
Outputs of similar age from Frontiers in Global Women's Health
#3
of 37 outputs
Altmetric has tracked 26,505,350 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 564 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one has done well, scoring higher than 88% 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 370,706 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 88% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.