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Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer

Overview of attention for article published in BioMed Research International, October 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

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5 X users

Citations

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33 Dimensions

Readers on

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72 Mendeley
Title
Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer
Published in
BioMed Research International, October 2017
DOI 10.1155/2017/8327980
Pubmed ID
Authors

Pedro Sernadela, Lorena González-Castro, Claudio Carta, Eelke van der Horst, Pedro Lopes, Rajaram Kaliyaperumal, Mark Thompson, Rachel Thompson, Núria Queralt-Rosinach, Estrella Lopez, Libby Wood, Agata Robertson, Claudia Lamanna, Mette Gilling, Michael Orth, Roxana Merino-Martinez, Manuel Posada, Domenica Taruscio, Hanns Lochmüller, Peter Robinson, Marco Roos, José Luís Oliveira

Abstract

Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges. Usually, these systems are developed as closed data silos, with independent formats and models, lacking comprehensive mechanisms to enable data sharing. To tackle these challenges, we developed a Semantic Web based solution that allows connecting distributed and heterogeneous registries, enabling the federation of knowledge between multiple independent environments. This semantic layer creates a holistic view over a set of anonymised registries, supporting semantic data representation, integrated access, and querying. The implemented system gave us the opportunity to answer challenging questions across disperse rare disease patient registries. The interconnection between those registries using Semantic Web technologies benefits our final solution in a way that we can query single or multiple instances according to our needs. The outcome is a unique semantic layer, connecting miscellaneous registries and delivering a lightweight holistic perspective over the wealth of knowledge stemming from linked rare disease patient registries.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 72 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 18%
Student > Master 10 14%
Other 8 11%
Researcher 7 10%
Student > Bachelor 5 7%
Other 11 15%
Unknown 18 25%
Readers by discipline Count As %
Computer Science 11 15%
Medicine and Dentistry 10 14%
Agricultural and Biological Sciences 9 13%
Biochemistry, Genetics and Molecular Biology 8 11%
Nursing and Health Professions 3 4%
Other 13 18%
Unknown 18 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 28 July 2018.
All research outputs
#7,941,190
of 25,382,440 outputs
Outputs from BioMed Research International
#2,519
of 10,760 outputs
Outputs of similar age
#121,240
of 340,057 outputs
Outputs of similar age from BioMed Research International
#51
of 226 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 10,760 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. 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 340,057 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 64% of its contemporaries.
We're also able to compare this research output to 226 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.