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Medical information-graphs, based on ontologies and FHIR

 
: Kober, Gerhard; Paschke, Adrian

:
Volltext urn:nbn:de:0011-n-6338199 (765 KByte PDF)
MD5 Fingerprint: 86796a4d5cc7745c8be45a1667f36674
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Erstellt am: 15.4.2021


Paschke, Adrian (Ed.); Rehm, Georg (Ed.); Al Qundus, Jamal (Ed.); Neudecker, Clemens (Ed.); Pintscher, Lydia (Ed.) ; Fraunhofer-Institut für Offene Kommunikationssysteme -FOKUS-, Berlin:
Qurator 2021, Conference on Digital Curation Technologies. Online resource : Proceedings of the Conference on Digital Curation Technologies (Qurator 2021); Berlin, Germany, February 8th to 12th, 2021
Berlin: CEUR, 2021 (CEUR Workshop Proceedings 2836)
URN: urn:nbn:de:0074-2836-1
Paper 14, 13 S.
Conference on Digital Curation Technologies (Qurator) <2, 2021, Online>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FOKUS ()
FHIR; RDF; ontologies

Abstract
Clinical relevant information about patients is stored in different locations and often hardly accessible for medical doctors and researchers. The treating doctor needs access to all available data from different sources, because not just locally available data but also data in remote locations and the relationships among different resources are relevant for patient care, e.g., the influence of a heart-rate on the medication intake. In this paper, we propose an approach for integrating FHIR (Fast Healthcare Interoperability Resources) for medical doctors’ or researchers’ unique needs/questions without changing the original data generated during a clinical medical process. Our approach combines semantic technologies, RDF (Resource Description Framework) data and OWL (Web Ontology Language) ontologies, with the medical standard FHIR, to transform medial information in a semantically annotated knowledge graph. The medical knowledge in the resulting graph is for the consumer (who needs the generated data) and for services of a “Distributed Medical Rule Engine” (DMRE). The service takes care of retrieving the information from different FHIR-stores, but also on the transformation to an RDF-graph. The resulting RDF-graph contains the clinical medical data, and also the connections between the entities. The knowledge graph contains applicable information for either a treating doctor or a researcher who, e.g., needs to explain observations and discover not-yet-explainable insights.

: http://publica.fraunhofer.de/dokumente/N-633819.html