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Intelligent diagnosis of the severity of disease conditions in COVID-19 patients based on the LASSO method

Overview of attention for article published in Frontiers in Public Health, March 2024
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About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 X user

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
2 Mendeley
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Title
Intelligent diagnosis of the severity of disease conditions in COVID-19 patients based on the LASSO method
Published in
Frontiers in Public Health, March 2024
DOI 10.3389/fpubh.2024.1302256
Pubmed ID
Authors

Zhuo Jiang, Aixiang Yang, Hao Chen, Yiqiu Shi, Xiaojing Li

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.
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 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 50%
Unknown 1 50%
Readers by discipline Count As %
Engineering 1 50%
Unknown 1 50%
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 31 March 2024.
All research outputs
#21,354,674
of 26,213,016 outputs
Outputs from Frontiers in Public Health
#8,315
of 14,638 outputs
Outputs of similar age
#238,780
of 345,991 outputs
Outputs of similar age from Frontiers in Public Health
#233
of 543 outputs
Altmetric has tracked 26,213,016 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,638 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one is in the 29th percentile – i.e., 29% 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 345,991 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 543 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.