Title |
Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer
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Published in |
BioMed Research International, October 2017
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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
Geographical breakdown
Country | Count | As % |
---|---|---|
New Zealand | 1 | 20% |
Unknown | 4 | 80% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 100% |
Mendeley readers
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% |