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Applying data mining techniques to improve diagnosis in neonatal jaundice

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
35 Dimensions

Readers on

mendeley
88 Mendeley
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Title
Applying data mining techniques to improve diagnosis in neonatal jaundice
Published in
BMC Medical Informatics and Decision Making, December 2012
DOI 10.1186/1472-6947-12-143
Pubmed ID
Authors

Duarte Ferreira, Abílio Oliveira, Alberto Freitas

Abstract

Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies.Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 1%
Unknown 87 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 22 25%
Researcher 12 14%
Student > Ph. D. Student 11 13%
Student > Bachelor 9 10%
Student > Doctoral Student 8 9%
Other 21 24%
Unknown 5 6%
Readers by discipline Count As %
Medicine and Dentistry 25 28%
Computer Science 23 26%
Engineering 7 8%
Nursing and Health Professions 6 7%
Agricultural and Biological Sciences 4 5%
Other 12 14%
Unknown 11 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 09 December 2012.
All research outputs
#2,534,080
of 11,214,021 outputs
Outputs from BMC Medical Informatics and Decision Making
#303
of 1,047 outputs
Outputs of similar age
#73,174
of 308,071 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#25
of 63 outputs
Altmetric has tracked 11,214,021 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,047 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 70% 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 308,071 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.