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A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance

Overview of attention for article published in BMC Medical Research Methodology, July 2022
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Mentioned by

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3 X users

Citations

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38 Dimensions

Readers on

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61 Mendeley
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Title
A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance
Published in
BMC Medical Research Methodology, July 2022
DOI 10.1186/s12874-022-01665-y
Pubmed ID
Authors

Hongxia Lu, Louis Ehwerhemuepha, Cyril Rakovski

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 10%
Unspecified 3 5%
Student > Bachelor 3 5%
Lecturer 3 5%
Lecturer > Senior Lecturer 2 3%
Other 7 11%
Unknown 37 61%
Readers by discipline Count As %
Computer Science 9 15%
Unspecified 3 5%
Medicine and Dentistry 3 5%
Mathematics 2 3%
Environmental Science 2 3%
Other 4 7%
Unknown 38 62%
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 04 July 2022.
All research outputs
#18,733,166
of 23,885,338 outputs
Outputs from BMC Medical Research Methodology
#1,769
of 2,124 outputs
Outputs of similar age
#294,870
of 423,904 outputs
Outputs of similar age from BMC Medical Research Methodology
#43
of 56 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,124 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 13th percentile – i.e., 13% 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 423,904 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.