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Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach

Overview of attention for article published in BMC Infectious Diseases, July 2019
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3 X users

Citations

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Readers on

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81 Mendeley
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Title
Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach
Published in
BMC Infectious Diseases, July 2019
DOI 10.1186/s12879-019-4282-y
Pubmed ID
Authors

Gleicy Macedo Hair, Flávio Fonseca Nobre, Patrícia Brasil

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users 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 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 81 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 15%
Researcher 8 10%
Student > Bachelor 8 10%
Student > Ph. D. Student 8 10%
Student > Postgraduate 5 6%
Other 12 15%
Unknown 28 35%
Readers by discipline Count As %
Medicine and Dentistry 9 11%
Computer Science 8 10%
Nursing and Health Professions 6 7%
Agricultural and Biological Sciences 5 6%
Biochemistry, Genetics and Molecular Biology 4 5%
Other 15 19%
Unknown 34 42%
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 24 July 2019.
All research outputs
#18,025,055
of 23,153,184 outputs
Outputs from BMC Infectious Diseases
#5,185
of 7,764 outputs
Outputs of similar age
#242,240
of 346,247 outputs
Outputs of similar age from BMC Infectious Diseases
#129
of 182 outputs
Altmetric has tracked 23,153,184 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,764 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one is in the 26th percentile – i.e., 26% 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 346,247 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 182 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.