Utilizing Real-World Data (RWD) for secondary use is still an open issue. Initiatives like OHDSI aim to tackle it by introducing a common data model (OMOP-CDM) to which data providers can opt to convert their data. While OMOP-CDM supports data interoperability and maintains a degree of intertwined terminologies/vocabularies, does not utilize the benefits of the Semantic Web technical paradigm. This paper presents an effort to convert the OMOPCDM to RDF format to further enhance its linked data capabilities.
R2RML is a language for expressing customized mappings from relational databases to RDF
datasets [
MIMIC-IV (Medical Information Mart for Intensive Care IV) is a large, and available upon-request
relational database that contains anonymized health data for over 40,000 Intensive Care Unit (ICU)
patients [
In general, each OMOP-CDM data table is mapped to a separate OWL class, while each table column corresponds to OWL properties: 1. Object properties: foreign keys from the initial source are mapped as object properties using a URI to link to a different individual 2. Data Properties: the majority of the numerical, string, date, etc fields from the initial source are mapped as Data Properties of the respective domain 3. Annotation Properties: fields that didn’t fall in the previous categories and usually contain information like the initial Vocabulary that a term derived from, such as ATC or MedDRA Regarding validation, a set of querying scripts was created to compare the source data (MIMIC-IV data in relational OMOP-CDM format) with the target data (MIMIC-IV data in OWL/RDF format).
Semantic-based ontologies are indispensable in HC for their role in promoting interoperability, supporting clinical and policy decision-making, while advancing medical research. As the HC industry, both applied and research, continues to evolve and embrace digital transformation, the adoption of semantic technologies is vital for unlocking the full potential of the collected RWD that can lead to direct improvements to patient outcomes and enhance the overall efficiency of HC systems.
A seamless transformation of the OMOP-CDM to a semantically enriched format means that all those sources can be easily converted to a format that benefits from capabilities provided by semantic knowledge modelling such as the ease of integration with other diverse data sources such as genetic profiling, signalling pathways, drug biochemistry, could lead to the identification of latent relationships and patterns, elevating the usage of RWD to a higher level. 4. References