Abstract
BackgroundSystematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) has been designed as standard clinical terminology for annotating Electronic Health Records (EHRs). EHRs textual information is used to classify patients’ diseases into an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) category (usually by an expert). Improving the accuracy of classification is the main purpose of using ontologies and OWL representations at the core of classification systems. In the last few years some ontologies and OWL representations for representing ICD-10-CM categories have been developed. However, they were not designed to be the basis for an automatic classification tool nor do they model ICD-10-CM inclusion terms as Web Ontology Language (OWL) axioms, which enables automatic classification. In this context we have developed Dione, an OWL representation of ICD-10-CM.ResultsDione is the first OWL representation of ICD-10-CM, which is logically consistent, whose axioms define the ICD-10-CM inclusion terms by means of a methodology based on SNOMED CT/ICD-10-CM mappings. The ICD-10-CM exclusions are handled with these mappings. Dione currently contains 391,669 classes, 391,720 entity annotation axioms and 11,795 owl:equivalentClass axioms which have been constructed using 104,646 relationships extracted from the SNOMED CT/ICD-10-CM and BioPortal mappings included in Dione using the owl:intersectionOf and the owl:someValuesFrom statements. The resulting OWL representation has been classified and its consistency tested with the ELK reasoner. We have also taken three clinical records from the Virgen de la Victoria Hospital (Málaga, Spain) which have been manually annotated using SNOMED CT. These annotations have been included as instances to be classified by the reasoner. The classified instances show that Dione could be a promising ICD-10-CM OWL representation to support the classification of patients’ diseases.ConclusionsDione is a first step towards the automatic classification of patients’ diseases by using SNOMED CT annotations embedded in Electronic Health Records (EHRs). The purpose of Dione is to standardise and formalise a medical terminology, thereby enabling new kinds of tools and new sets of functionalities to be developed. This in turn assists health specialists by providing classified information from EHRs and enables the automatic annotation of patients’ diseases with ICD-10-CM codes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0105-x) contains supplementary material, which is available to authorized users.
Highlights
The International Classification of Diseases, 10th Revision (ICD-10) [1] is a standard diagnostic tool for health management, epidemiology and clinical purposes
The main hypothesis of the work presented here is: (H1) It is possible to code, as OWL axioms, the ICD-10-CM inclusion terms obtained from SNOMED Systematized nomenclature of medicine - clinical terms (CT)/ICD-10CM mappings and use these OWL axioms to build an OWL representation of the ICD-10-CM diseases
In this formal representation of the ICD-10, anatomical entities were taken from the Foundational Model of Anatomy (FMA) [18], morphological abnormalities and procedures were taken from SNOMED CT, the organisms used were from the biological taxonomy and the chemical objects were taken from the International Union of Pure and Applied Chemistry nomenclature (IUPAC)
Summary
The International Classification of Diseases, 10th Revision (ICD-10) [1] is a standard diagnostic tool for health management, epidemiology and clinical purposes. It has to be said that the methodology adopted by the authors to formalise the ICD-10 has some limitations: first, only two ICD-10 chapters are represented; second, not all the ICD terms are represented using GALEN and the ontology was not loaded into an OWL reasoner and the formal consistency was neither checked nor classified Given these problems, the authors presented a DOLCE-based formal representation [17]. DOLCE is a descriptive upper-level ontology designed for ontology cleaning and interoperability In this formal representation of the ICD-10, anatomical entities were taken from the Foundational Model of Anatomy (FMA) [18], morphological abnormalities and procedures were taken from SNOMED CT, the organisms used were from the biological taxonomy and the chemical objects were taken from the International Union of Pure and Applied Chemistry nomenclature (IUPAC). The ontology has not been checked or classified by a reasoner
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