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Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis

Overview of attention for article published in BioData Mining, August 2021
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Mentioned by

twitter
1 X user

Citations

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13 Dimensions

Readers on

mendeley
33 Mendeley
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Title
Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis
Published in
BioData Mining, August 2021
DOI 10.1186/s13040-021-00276-5
Pubmed ID
Authors

Zhixuan Zeng, Shuo Yao, Jianfei Zheng, Xun Gong

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 readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 9%
Student > Doctoral Student 2 6%
Student > Master 2 6%
Professor 1 3%
Other 1 3%
Other 2 6%
Unknown 22 67%
Readers by discipline Count As %
Medicine and Dentistry 3 9%
Computer Science 3 9%
Engineering 2 6%
Chemical Engineering 1 3%
Arts and Humanities 1 3%
Other 3 9%
Unknown 20 61%
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 18 August 2021.
All research outputs
#18,809,260
of 23,310,485 outputs
Outputs from BioData Mining
#265
of 313 outputs
Outputs of similar age
#288,652
of 400,551 outputs
Outputs of similar age from BioData Mining
#11
of 11 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 313 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 6th percentile – i.e., 6% 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 400,551 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 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.