Modeling the International Classification of Diseases (ICD-10) in OWL

  • Manuel Möller
  • Daniel Sonntag
  • Patrick Ernst
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 272)


Current efforts in healthcare focus on establishing interoperability and data integration of medical resources for better collaboration between medical personal and doctors, especially in the patient treatment process. In covering human diseases, one of the major international standards in clinical practice is the International Classification for Diseases (ICD), maintained by the World Health Organization (WHO). Several country- and language-specific adaptations exist which share the general structure of the WHO version but differ in certain details. This complicates the exchange of patient records and hampers data integration across language borders. We present our approach for modeling the hierarchy of the ICD-10 using the Web Ontology Language (OWL). OWL, which we will introduce shortly, should provide a formal ontological basis for ICD-10 with enough expressivity to model interoperability and data integration of several medical resources such as ICD. Our resulting model captures the hierarchical information of the ICD-10 as well as comprehensive class labels for English and German. Specialities such as “Exclusion” statements, which make statements about the disjointness of certain ICD-10 categories, are modeled in a formal way. For properties which exceed the expressivity of OWL-DL, we provide a separate OWL-Full component which allows us to use the hierarchical knowledge and class labels with existing OWL-DL reasoners and capture the additional information in a Semantic Web format.


Merging Process Formal Ontological Basis Nutritional Anaemia Super Class Hierarchical Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Manuel Möller
    • 1
  • Daniel Sonntag
    • 1
  • Patrick Ernst
    • 1
  1. 1.German Research Center for AI (DFKI)SaarbrückenGermany

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