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Towards Integrative Machine Learning and Knowledge Extraction

Overview of attention for book
Attention for Chapter 9: Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

twitter
84 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
58 Mendeley
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Chapter title
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
Chapter number 9
Book title
Towards Integrative Machine Learning and Knowledge Extraction
Published in
arXiv, June 2017
DOI 10.1007/978-3-319-69775-8_9
Book ISBNs
978-3-31-969774-1, 978-3-31-969775-8
Authors

Keith Feldman, Louis Faust, Xian Wu, Chao Huang, Nitesh V. Chawla

Twitter Demographics

The data shown below were collected from the profiles of 84 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 31%
Researcher 9 16%
Student > Master 5 9%
Student > Doctoral Student 4 7%
Student > Bachelor 3 5%
Other 11 19%
Unknown 8 14%
Readers by discipline Count As %
Computer Science 26 45%
Medicine and Dentistry 5 9%
Business, Management and Accounting 3 5%
Engineering 3 5%
Nursing and Health Professions 2 3%
Other 10 17%
Unknown 9 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 55. 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 20 October 2019.
All research outputs
#625,780
of 22,101,977 outputs
Outputs from arXiv
#8,026
of 894,478 outputs
Outputs of similar age
#16,579
of 343,278 outputs
Outputs of similar age from arXiv
#219
of 22,338 outputs
Altmetric has tracked 22,101,977 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 894,478 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 99% 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 343,278 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 95% of its contemporaries.
We're also able to compare this research output to 22,338 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.