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An imConvNet-based deep learning model for Chinese medical named entity recognition

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2022
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users

Citations

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2 Dimensions

Readers on

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19 Mendeley
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Title
An imConvNet-based deep learning model for Chinese medical named entity recognition
Published in
BMC Medical Informatics and Decision Making, November 2022
DOI 10.1186/s12911-022-02049-4
Pubmed ID
Authors

Yuchen Zheng, Zhenggong Han, Yimin Cai, Xubo Duan, Jiangling Sun, Wei Yang, Haisong Huang

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 16%
Librarian 1 5%
Other 1 5%
Unspecified 1 5%
Student > Doctoral Student 1 5%
Other 3 16%
Unknown 9 47%
Readers by discipline Count As %
Medicine and Dentistry 2 11%
Unspecified 1 5%
Agricultural and Biological Sciences 1 5%
Nursing and Health Professions 1 5%
Social Sciences 1 5%
Other 1 5%
Unknown 12 63%
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 26 November 2022.
All research outputs
#19,416,201
of 23,885,338 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,615
of 2,048 outputs
Outputs of similar age
#325,576
of 449,670 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#20
of 37 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,048 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 9th percentile – i.e., 9% 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 449,670 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.