Publication:
Validating EHR clinical models using ontology patterns

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Date
2017-12
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Authors
Martínez Costa, Catalina ; Schulz, Stefan
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Publisher
Elsevier
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Informática y Sistemas
DOI
https://doi.org/10.1016/j.jbi.2017.11.001
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info:eu-repo/semantics/preprint
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
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.
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Semantic interoperability , Clinical models , Ontology design patterns , Data shapes , SHACL
Citation
Journal of Biomedical Informatics, 2017, Vol. 76, pp. 124-137
URI
http://hdl.handle.net/10201/149948
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