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Predicting asthma control deterioration in children

Overview of attention for article published in BMC Medical Informatics and Decision Making, October 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
1 news outlet
twitter
7 tweeters

Citations

dimensions_citation
33 Dimensions

Readers on

mendeley
93 Mendeley
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Title
Predicting asthma control deterioration in children
Published in
BMC Medical Informatics and Decision Making, October 2015
DOI 10.1186/s12911-015-0208-9
Pubmed ID
Authors

Gang Luo, Bryan L. Stone, Bernhard Fassl, Christopher G. Maloney, Per H. Gesteland, Sashidhar R. Yerram, Flory L. Nkoy

Abstract

Pediatric asthma affects 7.1 million American children incurring an annual total direct healthcare cost around 9.3 billion dollars. Asthma control in children is suboptimal, leading to frequent asthma exacerbations, excess costs, and decreased quality of life. Successful prediction of risk for asthma control deterioration at the individual patient level would enhance self-management and enable early interventions to reduce asthma exacerbations. We developed and tested the first set of models for predicting a child's asthma control deterioration one week prior to occurrence. We previously reported validation of the Asthma Symptom Tracker, a weekly asthma self-monitoring tool. Over a period of two years, we used this tool to collect a total of 2912 weekly assessments of asthma control on 210 children. We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child's asthma control deterioration one week ahead. Our best model achieved an accuracy of 71.8 %, a sensitivity of 73.8 %, a specificity of 71.4 %, and an area under the receiver operating characteristic curve of 0.757. We also identified potential improvements to our models to stimulate future research on this topic. Our best model successfully predicted a child's asthma control level one week ahead. With adequate accuracy, the model could be integrated into electronic asthma self-monitoring systems to provide real-time decision support and personalized early warnings of potential asthma control deteriorations.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 93 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 17%
Student > Ph. D. Student 15 16%
Student > Bachelor 12 13%
Student > Doctoral Student 9 10%
Student > Master 9 10%
Other 16 17%
Unknown 16 17%
Readers by discipline Count As %
Medicine and Dentistry 30 32%
Nursing and Health Professions 9 10%
Computer Science 9 10%
Engineering 7 8%
Social Sciences 4 4%
Other 15 16%
Unknown 19 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 06 May 2016.
All research outputs
#573,234
of 7,659,635 outputs
Outputs from BMC Medical Informatics and Decision Making
#52
of 933 outputs
Outputs of similar age
#26,567
of 241,611 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#2
of 37 outputs
Altmetric has tracked 7,659,635 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 933 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 94% 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 241,611 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 88% of its contemporaries.
We're also able to compare this research output to 37 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 94% of its contemporaries.