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Data reduction techniques for highly imbalanced medicare Big Data

Overview of attention for article published in Journal of Big Data, January 2024
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#8 of 384)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
12 news outlets
blogs
2 blogs

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
13 Mendeley
Title
Data reduction techniques for highly imbalanced medicare Big Data
Published in
Journal of Big Data, January 2024
DOI 10.1186/s40537-023-00869-3
Authors

John T. Hancock, Huanjing Wang, Taghi M. Khoshgoftaar, Qianxin Liang

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 3 23%
Lecturer > Senior Lecturer 1 8%
Lecturer 1 8%
Researcher 1 8%
Professor 1 8%
Other 1 8%
Unknown 5 38%
Readers by discipline Count As %
Computer Science 4 31%
Unspecified 3 23%
Social Sciences 1 8%
Unknown 5 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 90. 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 13 February 2024.
All research outputs
#470,344
of 25,364,653 outputs
Outputs from Journal of Big Data
#8
of 384 outputs
Outputs of similar age
#5,984
of 304,979 outputs
Outputs of similar age from Journal of Big Data
#1
of 5 outputs
Altmetric has tracked 25,364,653 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 384 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.7. This one has done particularly well, scoring higher than 98% 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 304,979 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 5 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