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An interpretable mortality prediction model for COVID-19 patients

Overview of attention for article published in Nature Machine Intelligence, May 2020
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#4 of 486)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Citations

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535 Dimensions

Readers on

mendeley
661 Mendeley
Title
An interpretable mortality prediction model for COVID-19 patients
Published in
Nature Machine Intelligence, May 2020
DOI 10.1038/s42256-020-0180-7
Authors

Li Yan, Hai-Tao Zhang, Jorge Goncalves, Yang Xiao, Maolin Wang, Yuqi Guo, Chuan Sun, Xiuchuan Tang, Liang Jing, Mingyang Zhang, Xiang Huang, Ying Xiao, Haosen Cao, Yanyan Chen, Tongxin Ren, Fang Wang, Yaru Xiao, Sufang Huang, Xi Tan, Niannian Huang, Bo Jiao, Cheng, Yong Zhang, Ailin Luo, Laurent Mombaerts, Junyang Jin, Zhiguo Cao, Shusheng Li, Hui Xu, Ye Yuan

Twitter Demographics

The data shown below were collected from the profiles of 986 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 661 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 661 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 96 15%
Student > Ph. D. Student 86 13%
Student > Master 79 12%
Student > Bachelor 69 10%
Other 36 5%
Other 144 22%
Unknown 151 23%
Readers by discipline Count As %
Medicine and Dentistry 120 18%
Computer Science 108 16%
Engineering 51 8%
Biochemistry, Genetics and Molecular Biology 46 7%
Agricultural and Biological Sciences 23 3%
Other 125 19%
Unknown 188 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 973. 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 09 April 2022.
All research outputs
#12,442
of 21,806,258 outputs
Outputs from Nature Machine Intelligence
#4
of 486 outputs
Outputs of similar age
#644
of 299,405 outputs
Outputs of similar age from Nature Machine Intelligence
#1
of 29 outputs
Altmetric has tracked 21,806,258 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 486 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 69.6. This one has done particularly well, scoring higher than 99% 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 299,405 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 29 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 99% of its contemporaries.