Automated enhancement of description logic-defined terminologies to facilitate mapping to ICD9-CM
Journal of Biomedical Informatics
Mapping the Gene Ontology into the Unified Medical Language System: Research Papers
Comparative and Functional Genomics
Semantic enrichment for medical ontologies
Journal of Biomedical Informatics
Non-standard reasoning services for the debugging of description logic terminologies
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Debugging unsatisfiable classes in OWL ontologies
Web Semantics: Science, Services and Agents on the World Wide Web
A Lexical-Ontological Resource for Consumer Heathcare
ISWC '09 Proceedings of the 8th International Semantic Web Conference
A hybrid methodology for consumer-oriented healthcare knowledge acquisition
KR4HC'09 Proceedings of the 2009 AIME international conference on Knowledge Representation for Health-Care: data, Processes and Guidelines
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Medical classification systems provide an essential instrument for unambiguously labeling clinical concepts in processes and services in healthcare and for improving the accessibility and elaboration of the medical content in clinical information systems. Over the last two decades the standardization efforts have established a number of classification systems as well as conversion mappings between them. Although these mappings represent the agreement reached between human specialists who devised them, there is no explicit formal reference establishing the precise meaning of the mappings. In this work we close this semantic gap by applying the results that have been recently reached in the area of AI and the Semantic Web on the formalization and analysis of mappings between heterogeneous conceptualizations. Practically, we focus on two classification systems which have received great widespread and preference within the European Union, namely ICPC-2 (International Classification of Primary Care) and ICD-10 (International Classification of Diseases). The particular contributions of this work are: the logical encoding in OWL of ICPC-2 and ICD-10 classifications; the formalization of the existing ICPC-ICD conversion mappings in terms of OWL axioms and further verification of its coherence using the logical reasoning; and finally, the outline of the other semantic techniques for automated analysis of implications of future mapping changes between ICPC and ICD classifications.