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Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study

Overview of attention for article published in Journal of Translational Medicine, October 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
39 Dimensions

Readers on

mendeley
62 Mendeley
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Title
Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study
Published in
Journal of Translational Medicine, October 2019
DOI 10.1186/s12967-019-2075-0
Pubmed ID
Authors

Xian-Fei Ding, Jin-Bo Li, Huo-Yan Liang, Zong-Yu Wang, Ting-Ting Jiao, Zhuang Liu, Liang Yi, Wei-Shuai Bian, Shu-Peng Wang, Xi Zhu, Tong-Wen Sun

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 13%
Student > Bachelor 8 13%
Student > Ph. D. Student 5 8%
Student > Doctoral Student 3 5%
Student > Postgraduate 3 5%
Other 10 16%
Unknown 25 40%
Readers by discipline Count As %
Medicine and Dentistry 11 18%
Computer Science 7 11%
Nursing and Health Professions 4 6%
Business, Management and Accounting 3 5%
Engineering 2 3%
Other 6 10%
Unknown 29 47%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 29 April 2021.
All research outputs
#7,656,930
of 23,310,485 outputs
Outputs from Journal of Translational Medicine
#1,282
of 4,114 outputs
Outputs of similar age
#137,596
of 350,485 outputs
Outputs of similar age from Journal of Translational Medicine
#25
of 61 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,114 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one has gotten more attention than average, scoring higher than 64% 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 350,485 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.