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The application of unsupervised deep learning in predictive models using electronic health records

Overview of attention for article published in BMC Medical Research Methodology, February 2020
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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10 X users

Citations

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17 Dimensions

Readers on

mendeley
53 Mendeley
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Title
The application of unsupervised deep learning in predictive models using electronic health records
Published in
BMC Medical Research Methodology, February 2020
DOI 10.1186/s12874-020-00923-1
Pubmed ID
Authors

Lei Wang, Liping Tong, Darcy Davis, Tim Arnold, Tina Esposito

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 19%
Student > Ph. D. Student 7 13%
Student > Doctoral Student 5 9%
Student > Bachelor 4 8%
Lecturer 3 6%
Other 5 9%
Unknown 19 36%
Readers by discipline Count As %
Computer Science 8 15%
Medicine and Dentistry 6 11%
Engineering 4 8%
Mathematics 2 4%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Other 7 13%
Unknown 24 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 26 April 2020.
All research outputs
#6,544,887
of 24,051,764 outputs
Outputs from BMC Medical Research Methodology
#977
of 2,135 outputs
Outputs of similar age
#117,806
of 363,717 outputs
Outputs of similar age from BMC Medical Research Methodology
#30
of 53 outputs
Altmetric has tracked 24,051,764 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 2,135 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 54% 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 363,717 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 67% of its contemporaries.
We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.