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EHR based Genetic Testing Knowledge Base (iGTKB) Development

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2015
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1 tweeter

Citations

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3 Dimensions

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36 Mendeley
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Title
EHR based Genetic Testing Knowledge Base (iGTKB) Development
Published in
BMC Medical Informatics and Decision Making, November 2015
DOI 10.1186/1472-6947-15-s4-s3
Pubmed ID
Authors

Qian Zhu, Hongfang Liu, Christopher G Chute, Matthew Ferber

Abstract

The gap between a large growing number of genetic tests and a suboptimal clinical workflow of incorporating these tests into regular clinical practice poses barriers to effective reliance on advanced genetic technologies to improve quality of healthcare. A promising solution to fill this gap is to develop an intelligent genetic test recommendation system that not only can provide a comprehensive view of genetic tests as education resources, but also can recommend the most appropriate genetic tests to patients based on clinical evidence. In this study, we developed an EHR based Genetic Testing Knowledge Base for Individualized Medicine (iGTKB).

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 17%
Researcher 6 17%
Student > Master 5 14%
Student > Bachelor 4 11%
Student > Doctoral Student 3 8%
Other 7 19%
Unknown 5 14%
Readers by discipline Count As %
Medicine and Dentistry 13 36%
Engineering 5 14%
Computer Science 4 11%
Agricultural and Biological Sciences 3 8%
Business, Management and Accounting 2 6%
Other 3 8%
Unknown 6 17%

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 03 December 2015.
All research outputs
#9,934,103
of 12,409,138 outputs
Outputs from BMC Medical Informatics and Decision Making
#927
of 1,122 outputs
Outputs of similar age
#226,555
of 333,127 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#127
of 150 outputs
Altmetric has tracked 12,409,138 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,122 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 2nd percentile – i.e., 2% 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 333,127 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 150 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.