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Comparison of different feature extraction methods for applicable automated ICD coding

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
27 Mendeley
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Title
Comparison of different feature extraction methods for applicable automated ICD coding
Published in
BMC Medical Informatics and Decision Making, January 2022
DOI 10.1186/s12911-022-01753-5
Pubmed ID
Authors

Zhao Shuai, Diao Xiaolin, Yuan Jing, Huo Yanni, Cui Meng, Wang Yuxin, Zhao Wei

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 4%
Librarian 1 4%
Other 1 4%
Lecturer 1 4%
Student > Bachelor 1 4%
Other 1 4%
Unknown 21 78%
Readers by discipline Count As %
Computer Science 2 7%
Unspecified 1 4%
Medicine and Dentistry 1 4%
Unknown 23 85%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 March 2024.
All research outputs
#8,629,662
of 25,613,746 outputs
Outputs from BMC Medical Informatics and Decision Making
#833
of 2,154 outputs
Outputs of similar age
#185,587
of 519,682 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#14
of 57 outputs
Altmetric has tracked 25,613,746 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,154 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 57% 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 519,682 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 59% of its contemporaries.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.