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Abstract/Summary
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HL7 Fast Healthcare Interoperability Resources (FHIR) is rapidly becoming the
standards framework for the exchange of electronic health record (EHR) data. By
leveraging FHIRs resource-oriented architecture, FHIR RDF stands to become
the first main-stream clinical data standard to incorporate the Semantic Web
vision. The combination of FHIR, knowledge graphs and the Semantic Web
enables a new paradigm to build classification and explainable artificial
intelligence (AI) applications in healthcare. The objective of the tutorial is to
introduce the FHIR RDF data transformation and validation framework, show
how to build clinical knowledge graphs (cKG) in FHIR RDF, and provide the
audience with hands-on opportunities on FHIR RDF and cKG tooling.
Specifically:Topics regarding the FHIR RDF data transformation and validation
framework include:
1. FHIR, and it´s representations FHIR JSON and FHIR RDF;
2. Conversion of FHIR JSON to FHIR RDF (via JSON-LD), use of the FHIR RDF
playground, command line tools and HAPI-FHIR´s RDF (Turtle) support;
3. The Shape Expressions (ShEx) schemafor FHIR and its use for validating
FHIR data;
4. FHIR structure definitions and their expression as JSON-LD contexts and
ShEx schemas;
In addition, the FHIR-Ontop-OMOP tool exposes the Observational Medical
Outcomes Partnership (OMOP) data as a queryable Knowledge Graph compliant
with the HL7 FHIR standard using the Ontop Virtual Knowledge Graph engine. In
this tutorial, we demonstrate how to set up Ontop over a working connection to
the OMOP PostgreSQL database using a mapping language. Thanks to the
virtual approach, the FHIR RDF triples populated by declarative mapping do not
need to be materialized. Instead, Ontop translates the SPARQL query over the
FHIR RDF model to a SQL query over the OMOP database. We will illustrate the
query translation process with some representative phenotype queries.
Acknowledgements:
This tutorial session is supported in part by the NIH FHIRCat R01 grant (R01
EB030529).
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