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

Möller, Manuel; Sonntag, Daniel; Erñst, Patrick

doi:10.1007/978-3-642-29764-9_16

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…In the Background section, we highlighted some limitations of existing work in the literature. In this section, we discuss the formal representation in OWL of ICD-10-CM proposed by [ 19 ] given that it is an ICD-10-CM representation that has improved upon other approaches. In this approach, the authors created an ICD-10-CM hierarchy with owl:subClassOf handling the ICD-10-CM exclusions with owl:disjointWith axioms.…”

Section: Resultsmentioning

confidence: 99%

“…The last approach to represent ICD-10 in OWL was developed in [ 19 ]. In this study, an ontology was created based on two super-classes, the icd10:Entry and the icd10:Modifier which contain ICD-10 codes from the WHO and the German Institute for Medical Documentation and Information (German: Deutsches Institut für Medizinische Dokumentation und Information) [ 20 ], respectively.…”

Section: Introductionmentioning

confidence: 99%

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Dione: An OWL representation of ICD-10-CM for classifying patients’ diseases

2016

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dione: An OWL representation of ICD-10-CM for classifying patients’ diseases

2016

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“…The heterogeneous and distributed nature of health data makes it a very suitable candidate for Semantic Web Applications [Rajbhandari et al 2012;Burgun et al 1999]. There have been recently some efforts to use ontologies to describe health information standards [Möller et al 2010]. The usage of ontologies should not be limited to just defining the domain.…”

Section: Methodology For Defining Working Health Ontologiesmentioning

confidence: 99%

A Methodology for Defining Working Healthcare Ontologies

2019

JSTR

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Retrieving the information needed for right clinical decision and searching through a large amount of data are important challenges that health information systems encountered despite the advances in the information systems. In order to reuse these distributed data, the health information standards are developed to enable interoperable health information systems. As the standards are far from the necessary semantics, the data defined according to these standards are not been efficiently used by machines. Semantic Web technologies provide a common framework to access and process the information by machines with the support of ontologies. The heterogeneous and distributed nature of health data makes it a very suitable candidate to define health domain specific ontologies. In this work, a methodology for defining health ontologies, which should work as information base of a health information system as the next stage, is proposed to ensure interoperability and reuse between health information systems with the support of health information standards. Clinical Biochemistry Laboratory Ontology (CBLO) is given as a use case to model information of laboratory tests realized in clinical biochemistry laboratory that are a sub-domain of medical laboratory tests. In addition, a weighted evaluation for the proposed methodology is presented.

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“…That is to say. The affective disorder may be another disease state's principal or secondary symptom (Moller et al, 2010). There is evidence that obesity, metabolic syndrome, and cardiovascular disease are widespread in patients diagnosed with affective disorders.…”

Section: The Leading Causes Of Affective Disordermentioning

confidence: 99%

“…The most common misdiagnoses are monophasic depression (60% of cases) and anxiety disorder (26%) (Mitchell et al, 2004). Despite the release of diagnostic guidelines (Moller et al, 2010), there are still many missed diagnoses or misdiagnoses. In addition, in March 2020, the coronavirus disease (COVID-19) will reach a global pandemic (Fleischmann et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Bibliometric and visual analysis of the global treatment of affective disorders in the 21st Century: Developments, hotspots, and trends

Wei

Zhong

2023

Preprint

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The treatment of affective disorders is becoming the mainstream of research, and its study has attracted extensive attention from scholars all over the world. However, there is no comprehensive review of this research. From the perspective of bibliometrics, based on 2692 articles in the Web of Science Core Collection (WOSCC) database, this paper systematically and comprehensively describes the progress and hot spots in the treatment of affective disorders. Since the 21st century, research on treating affective disorders has shown a growing trend. The results show that from 2000 to 2021, the United States was the most active country in publishing relevant articles, Harvard University was the most productive institution, and Ketter T was the most representative author in this field. However, there was little cooperation between different countries/institutions. The research in the field of affective disorder treatment can be summarized into three main research areas: the leading causes of affective disorder, the harm of affective disorder, and the treatment methods. Finally, this paper proposes some challenges and prospects for the rapid and balanced development of the field of affective disorder therapy. As far as we know, this study is the first one specialized in visualizing the treatment of affective disorders, which is conducive to identifying current research hotspots and predicting potential future research directions.

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

Cited by 4 publications

References 9 publications

Dione: An OWL representation of ICD-10-CM for classifying patients’ diseases

Dione: An OWL representation of ICD-10-CM for classifying patients’ diseases

A Methodology for Defining Working Healthcare Ontologies

Bibliometric and visual analysis of the global treatment of affective disorders in the 21st Century: Developments, hotspots, and trends

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