title: GALEN based formal representation of ICD10.
abstract: OBJECTIVE
  The main objective is to create a knowledge-intensive coding support tool for the International Classification of Diseases (ICD10), which is based on formal representation of ICD10 categories. Beyond this task the resulting ontology could be reused in various ways. Decidability is an important issue for computer-assisted coding; consequently the ontology should be represented in description logic.
  
  
  METHODS
  The meaning of the ICD10 categories is represented using the GALEN Core Reference Model. Due to the deficiencies of its representation language (GRAIL) the ontology is transformed to the quasi-standard OWL. A test system which extracts disease concepts and classifies them to ICD10 categories has been implemented in Prolog to verify the feasibility of the approach.
  
  
  RESULTS
  The formal representation of the first two chapters of ICD10 (infectious diseases and neoplasms) has been almost completed. The constructed ontology has been converted to OWL DL. The test system successfully identified diseases in medical records from gastrointestinal oncology (84% recall, however precision is only 45%). The classifier module is still under development. Due to the experiences gained during the modelling, in the future work FMA is going to be used as anatomical reference ontology.
authors.affiliation: Budapest University of Technology and Economics, Department of Measurement and Information Systems, Budapest, Hungary. heja@mit.bme.hu
authors.forename: Gergely
authors.initials: G
authors.lastname: Héja
authors.forename: György
authors.initials: G
authors.lastname: Surján
authors.forename: Gergely
authors.initials: G
authors.lastname: Lukácsy
authors.forename: Péter
authors.initials: P
authors.lastname: Pallinger
authors.forename: Miklós
authors.initials: M
authors.lastname: Gergely
mesh_terms: D000043:Abstracting and Indexing; D001185:Artificial Intelligence; D001331:Automation; D006801:Humans; D006814:Hungary; D038801:International Classification of Diseases; D009323:Natural Language Processing; D009626:Terminology as Topic; D018875:Vocabulary, Controlled