Validating EHR clinical models using ontology patterns

Martínez Costa, Catalina; Schulz, Stefan

Publication:
Validating EHR clinical models using ontology patterns

dc.contributor.author	Martínez Costa, Catalina
dc.contributor.author	Schulz, Stefan
dc.contributor.department	Informática y Sistemas
dc.date.accessioned	2025-02-02T17:52:02Z
dc.date.available	2025-02-02T17:52:02Z
dc.date.issued	2017-12
dc.description	© 2017 Elsevier Inc. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Submitted Published Manuscript version of a Published Work that appeared in final form in Journal of Biomedical Informatics . To access the final edited and published work see https://doi.org/10.1016/j.jbi.2017.11.001
dc.description.abstract	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.	es
dc.format	application/pdf	es
dc.format.extent	36	es
dc.identifier.citation	Journal of Biomedical Informatics, 2017, Vol. 76, pp. 124-137
dc.identifier.doi	https://doi.org/10.1016/j.jbi.2017.11.001
dc.identifier.issn	Print: 1532-0464
dc.identifier.uri	http://hdl.handle.net/10201/149948
dc.language	eng	es
dc.publisher	Elsevier	es
dc.relation	Initial work was supported by the EU Network of Excellence SemanticHealthNet (Semantic Interoperability for Health Network, FP7-FP7 Information and Communication Technologies-2011-7, Grant agreement 288408).	es
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S1532046417302381?via%3Dihub	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Semantic interoperability	es
dc.subject	Clinical models	es
dc.subject	Ontology design patterns	es
dc.subject	Data shapes	es
dc.subject	SHACL	es
dc.title	Validating EHR clinical models using ontology patterns	es
dc.type	info:eu-repo/semantics/preprint	es
dspace.entity.type	Publication	es