A Semantic Web Pragmatic Approach to Develop Clinical Ontologies, and thus Semantic Interoperability, based in HL7 v2.xml Messaging

A Semantic Web Pragmatic Approach to Develop Clinical Ontologies, and thus Semantic Interoperability, based in HL7 v2.xml Messaging

David Mendes (Universidade de Évora, Portugal) and Irene Pimenta Rodrigues (Universidade de Évora, Portugal)
DOI: 10.4018/978-1-4666-3667-5.ch014
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Abstract

The ISO/HL7 27931:2009 standard intends to establish a global interoperability framework for healthcare applications. However, being a messaging related protocol, it lacks a semantic foundation for interoperability at a machine treatable level intended through the Semantic Web. There is no alignment between the HL7 V2.xml message payloads and a meaning service like a suitable ontology. Careful application of Semantic Web tools and concepts can ease the path to the fundamental concept of Shared Semantics. In this chapter, the Semantic Web and Artificial Intelligence tools and techniques that allow aligned ontology population are presented and their applicability discussed. The authors present the coverage of HL7 RIM inadequacy for ontology mapping and how to circumvent it, NLP techniques for semi-automated ontology population, and the current trends about knowledge representation and reasoning that concur to the proposed achievement.
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2. Ontology Population In Health

The amount of Clinical data digitally preserved in EHRs is colossal, ever increasing and numerous problems have to be devised and solved as reviewed by Meystre et al. (2008) and Liu et al. (2010). Most of the clinical data is in text form coming either from typing entry, transcription from dictation or from speech recognition applications. Accurate coding is necessary for comparability, auditability, and last but not least important, accountability. We will figure out a “picture of Healthcare provisioning” through clear identification of the meaning of the available data and not only by the capability of cataloging and codifying that huge amount of data.

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