Semantic modelling of common data elements for rare disease registries, and a prototype workflow for their deployment over registry data

Abstract

Background: The European Platform on Rare Disease Registration (EU RD Platform) aims to address the fragmentation of European rare disease (RD) patient data, scattered among hundreds of independent and non-coordinating registries, by establishing standards for integration and interoperability. The first practical output of this effort was a set of 16 Common Data Elements (CDEs) that should be implemented by all RD registries. Interoperability, however, requires decisions beyond data elements - including data models, formats, and semantics. Within the European Joint Programme on Rare Diseases (EJP RD), we aim to further the goals of the EU RD Platform by generating reusable RD semantic model templates that follow the FAIR Data Principles.

Results: Through a team-based iterative approach, we created semantically grounded models to represent each of the CDEs, using the SemanticScience Integrated Ontology as the core framework for representing the entities and their relationships. Within that framework, we mapped the concepts represented in the CDEs, and their possible values, into domain ontologies such as the Orphanet Rare Disease Ontology, Human Phenotype Ontology and National Cancer Institute Thesaurus. Finally, we created an exemplar, reusable ETL pipeline that we will be deploying over these non-coordinating data repositories to assist them in creating model-compliant FAIR data without requiring site-specific coding nor expertise in Linked Data or FAIR.

Conclusions: Within the EJP RD project, we determined that creating reusable, expert-designed templates reduced or eliminated the requirement for our participating biomedical domain experts and rare disease data hosts to understand OWL semantics. This enabled them to publish highly expressive FAIR data using tools and approaches that were already familiar to them.

Keywords: Common data elements; Data transformation; Disease registries; FAIR data; Interoperability; Linked data; Ontologies; Rare disease; Semantic web.

Fig. 1

Conceptual diagram of the overall SIO model to be applied to the CDEs. It is centred around five primary elements – identifiers, entities (physical and information-content), roles, processes, and attributes. In the diagram, we provide hypothetical examples of the specific ontological types that might be associated with each element

Fig. 2

The Markdown documentation explaining how to prepare a CSV file for the “Patient Status” CDE. Documentation includes, where appropriate, the restrictions on the possible values in a given column, such as ‘status uri’ in this example

Fig. 3

Visualization of an exemplar RDF instance for the “Patient Status” CDE (CDE 3.1 & 3.2)

Fig. 4

Visualization of the ShEx validation shape for the Patient Status CDE data

Fig. 5

The components of the workflow annotated with the responsibilities of the parties. The left side of the diagram, outlined in green, are the responsibilities of the data custodian in collaboration with the Data Steward. This includes export of their registry data into CSV format, and possibly some additional modification of that exported data to conform to the template. On the right is the fully automated CDE-in-a-Box, which is constructed by the FAIR Expert team and provided as a docker-compose installation. The arrow labelled “trigger” is the Web page call that the data custodian makes when they are ready to execute their transformation

Fig. 6

The model for Laboratory Measurements. Of note are the three new connections on the “Quantitation” (Process) node – one representing the input (blood), one representing the target molecule (haemoglobin), and the third representing the link to the protocol. The remainder of the model is (structurally) identical to the core model shown in Fig. 1