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Martínez-Costa, C; Schulz, S.
Validating EHR clinical models using ontology patterns.
J Biomed Inform. 2017; 76(20):124-137 [OPEN ACCESS]
Web of Science PubMed FullText FullText_MUG


Authors Med Uni Graz:
Martinez Costa Catalina
Schulz Stefan

Dimensions Citations:

Plum Analytics:
Clinical models are artefacts that specify how information is structured in electronic health records (EHRs). However, the makeup of clinical models is not guided by any formal constraint beyond a semantically vague information model. We address this gap by advocating ontology design patterns as a mechanism that makes the semantics of clinical models explicit. This paper demonstrates how ontology design patterns can validate existing clinical models using SHACL. Based on the Clinical Information Modelling Initiative (CIMI), we show how ontology patterns detect both modeling and terminology binding errors in CIMI models. SHACL, a W3C constraint language for the validation of RDF graphs, builds on the concept of "Shape", a description of data in terms of expected cardinalities, datatypes and other restrictions. SHACL, as opposed to OWL, subscribes to the Closed World Assumption (CWA) and is therefore more suitable for the validation of clinical models. We have demonstrated the feasibility of the approach by manually describing the correspondences between six CIMI clinical models represented in RDF and two SHACL ontology design patterns. Using a Java-based SHACL implementation, we found at least eleven modeling and binding errors within these CIMI models. This demonstrates the usefulness of ontology design patterns not only as a modeling tool but also as a tool for validation. Copyright © 2017 Elsevier Inc. All rights reserved.
Find related publications in this database (using NLM MeSH Indexing)
Artifacts -
Electronic Health Records -
Humans -
Models, Theoretical -
Terminology as Topic -

Find related publications in this database (Keywords)
Semantic interoperability
Clinical models
Ontology design patterns
Data shapes
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