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

Biomed Res Int. 2017;2017:8327980. doi: 10.1155/2017/8327980. Epub 2017 Oct 29.


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.

MeSH terms

  • Computational Biology / methods
  • Database Management Systems / statistics & numerical data*
  • Databases, Factual / statistics & numerical data
  • Humans
  • Information Dissemination / methods
  • Information Storage and Retrieval / statistics & numerical data*
  • Internet / statistics & numerical data
  • Rare Diseases / epidemiology*
  • Registries / statistics & numerical data*
  • Semantic Web / statistics & numerical data*
  • Software / statistics & numerical data