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AGUIA: autonomous graphical user interface assembly for clinical trials semantic data services

Overview of attention for article published in BMC Medical Informatics and Decision Making, October 2010
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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 (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

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1 patent
wikipedia
1 Wikipedia page

Citations

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4 Dimensions

Readers on

mendeley
48 Mendeley
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1 CiteULike
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Title
AGUIA: autonomous graphical user interface assembly for clinical trials semantic data services
Published in
BMC Medical Informatics and Decision Making, October 2010
DOI 10.1186/1472-6947-10-65
Pubmed ID
Authors

Miria C Correa, Helena F Deus, Ana T Vasconcelos, Yuki Hayashi, Jaffer A Ajani, Srikrishna V Patnana, Jonas S Almeida

Abstract

AGUIA is a front-end web application originally developed to manage clinical, demographic and biomolecular patient data collected during clinical trials at MD Anderson Cancer Center. The diversity of methods involved in patient screening and sample processing generates a variety of data types that require a resource-oriented architecture to capture the associations between the heterogeneous data elements. AGUIA uses a semantic web formalism, resource description framework (RDF), and a bottom-up design of knowledge bases that employ the S3DB tool as the starting point for the client's interface assembly. The data web service, S3DB, meets the necessary requirements of generating the RDF and of explicitly distinguishing the description of the domain from its instantiation, while allowing for continuous editing of both. Furthermore, it uses an HTTP-REST protocol, has a SPARQL endpoint, and has open source availability in the public domain, which facilitates the development and dissemination of this application. However, S3DB alone does not address the issue of representing content in a form that makes sense for domain experts. We identified an autonomous set of descriptors, the GBox, that provides user and domain specifications for the graphical user interface. This was achieved by identifying a formalism that makes use of an RDF schema to enable the automatic assembly of graphical user interfaces in a meaningful manner while using only resources native to the client web browser (JavaScript interpreter, document object model). We defined a generalized RDF model such that changes in the graphic descriptors are automatically and immediately (locally) reflected into the configuration of the client's interface application. The design patterns identified for the GBox benefit from and reflect the specific requirements of interacting with data generated by clinical trials, and they contain clues for a general purpose solution to the challenge of having interfaces automatically assembled for multiple and volatile views of a domain. By coding AGUIA in JavaScript, for which all browsers include a native interpreter, a solution was found that assembles interfaces that are meaningful to the particular user, and which are also ubiquitous and lightweight, allowing the computational load to be carried by the client's machine.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Germany 2 4%
Sweden 1 2%
Unknown 43 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 23%
Other 7 15%
Student > Ph. D. Student 7 15%
Student > Master 6 13%
Student > Bachelor 3 6%
Other 6 13%
Unknown 8 17%
Readers by discipline Count As %
Computer Science 10 21%
Agricultural and Biological Sciences 7 15%
Medicine and Dentistry 6 13%
Nursing and Health Professions 6 13%
Arts and Humanities 2 4%
Other 7 15%
Unknown 10 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 31 August 2023.
All research outputs
#5,240,914
of 24,682,395 outputs
Outputs from BMC Medical Informatics and Decision Making
#481
of 2,104 outputs
Outputs of similar age
#22,386
of 103,863 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#4
of 17 outputs
Altmetric has tracked 24,682,395 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,104 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 76% 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 103,863 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.