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Survey on RNN and CRF models for de-identification of medical free text

Overview of attention for article published in Journal of Big Data, September 2020
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
22 Mendeley
Title
Survey on RNN and CRF models for de-identification of medical free text
Published in
Journal of Big Data, September 2020
DOI 10.1186/s40537-020-00351-4
Authors

Joffrey L. Leevy, Taghi M. Khoshgoftaar, Flavio Villanustre

Twitter Demographics

The data shown below were collected from the profiles of 3 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 3 14%
Researcher 2 9%
Student > Ph. D. Student 2 9%
Student > Master 1 5%
Lecturer 1 5%
Other 2 9%
Unknown 11 50%
Readers by discipline Count As %
Computer Science 9 41%
Linguistics 1 5%
Social Sciences 1 5%
Engineering 1 5%
Unknown 10 45%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 16 December 2020.
All research outputs
#11,924,876
of 19,770,032 outputs
Outputs from Journal of Big Data
#143
of 290 outputs
Outputs of similar age
#166,102
of 313,928 outputs
Outputs of similar age from Journal of Big Data
#1
of 1 outputs
Altmetric has tracked 19,770,032 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 290 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.8. This one is in the 49th percentile – i.e., 49% 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 313,928 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them