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Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2019
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Mentioned by

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1 X user

Citations

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Readers on

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87 Mendeley
Title
Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison
Published in
BMC Medical Informatics and Decision Making, April 2019
DOI 10.1186/s12911-019-0783-2
Pubmed ID
Authors

Yaoyun Zhang, Firat Tiryaki, Min Jiang, Hua Xu

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 87 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 13%
Student > Ph. D. Student 10 11%
Student > Master 9 10%
Other 4 5%
Student > Doctoral Student 4 5%
Other 16 18%
Unknown 33 38%
Readers by discipline Count As %
Medicine and Dentistry 14 16%
Psychology 8 9%
Nursing and Health Professions 5 6%
Computer Science 4 5%
Engineering 4 5%
Other 16 18%
Unknown 36 41%
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 05 April 2019.
All research outputs
#20,564,621
of 23,140,503 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,827
of 2,016 outputs
Outputs of similar age
#301,873
of 351,526 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#36
of 46 outputs
Altmetric has tracked 23,140,503 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,016 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 1st percentile – i.e., 1% 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 351,526 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.