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

Roldán‐García, María del Mar; García-Godoy, María Jesús; Aldana‐Montes, José F.

doi:10.1186/s13326-016-0105-x

Cited by 16 publications

(10 citation statements)

References 19 publications

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“…Study [18] used word embedding combined with a convolutional neural network (CCN), which showed outstanding performance compared with the NLP plus supervised machine learning models. A prior study [19] automatically classify patients' diseases into an ICD-10-CM category based on the well known Web Ontology Language (OWL). Another study [20] combined three coding systems into a single superior system to improve the performance of automated ICD-9-CM codes in clinical reports.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Semi-automatic ICD-10 Coding System

Zhou

Cheng

et al. 2020

Preprint

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BackgroundThe International Classification of Diseases, 10th Revision (ICD-10) has been widely used to describe the diagnosis information of patients. Automatic ICD-10 coding is important because manually assigning codes is expensive, time consuming and error prone. Although numerous approaches have been developed to explore automatic coding, few of them have been applied in practice. Our aim is to construct a practical, automatic ICD-10 coding machine to improve coding efficiency and quality in daily work. MethodsIn this study, we propose the use of regular expressions (regexps) to establish a correspondence between diagnosis codes and diagnosis descriptions in outpatient settings and at admission and discharge. The description models of the regexps were embedded in our upgraded coding system, which queries a diagnosis description and assigns a unique diagnosis code. Like most studies, the precision (P), recall (R), F-measure (F) and overall accuracy (A) were used to evaluate the system performance. Our study had two stages. The datasets were obtained from the diagnosis information on the homepage of the discharge medical record. The testing sets were from ResultsThe values of P were 89.27% and 88.38% in the first testing phase and the second testing phase, respectively, which demonstrate high precision. The automatic ICD-10 coding system completed more than 160,000 codes in 16 months, which reduced the workload of the coders. In addition, a comparison between the amount of time needed for manual coding and automatic coding indicated the effectiveness of the system-the time needed for automatic coding takes nearly 100 times less than manual coding. ConclusionsOur automatic coding system is well suited for the coding task. Further studies are warranted to perfect the description models of the regexps and to develop synthetic approaches to improve system

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Section: Introductionmentioning

confidence: 99%

Construction of a Semi-automatic ICD-10 Coding System

Zhou

Cheng

et al. 2020

Preprint

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“…The current version of ICD-10 has 12,445 concepts with only one object property. ICD-10 CM [Del Mar Roldán-Garcia et al 2016] and ICD-10 PCS [ICD-10 PCS 2019] ontologies are also developed on the basis of ICD-10 ontology for specific clinical domains. The beta version of ICD-11 [Tudorache et al 2013] is issued in 2017.…”

Section: Literature Reviewmentioning

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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“…B. die Logical Observation Identifiers Names and Codes -LOINC, Systematized Nomenclature of Medicine -SNOMED, häufig aber auch eigene Nomenklaturen) verwenden, ist die Interoperabilität mit anderen Klassifikationssystemen als ICD ein nicht zu unterschätzender Faktor für die erfolgreiche Implementierung von DEMIS. Dafür ist ein Mapping zwischen den verschiedenen Klassifikationssystemen unter Nutzung semantischer Verfahren erforderlich [17,18].…”

Section: Elektronische Meldung (Demis)unclassified

Infektionskrankheiten und ihre Codierung

Diercke

Beermann

Tolksdorf

et al. 2018

Bundesgesundheitsbl

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The revision of the International Classification of Diseases (ICD) could change morbidity and mortality statistics significantly, which also affects the area of infectious diseases. Infectious diseases are classified according to their etiology, affected body system or the life period during which the episode occurs. Specific challenges arise from emerging pathogens and the respective necessary adaptation. For epidemiologic analysis ICD-10 does not always offer enough additional information.ICD provides the basis for international comparison of infectious disease morbidity and mortality statistics, but it is also used to collect data for surveillance and research purposes, e. g. the notification system for infectious diseases, syndromic surveillance systems and the evaluation of data quality by using secondary data sources.ICD-11 offers the chance to better represent epidemiological concepts of infectious diseases by adding more relevant information as affected body system or manifestation. Due to the complexity of coding, ensuring continuity of morbidity and mortality statistics could be challenging.

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

Cited by 16 publications

References 19 publications

Construction of a Semi-automatic ICD-10 Coding System

Construction of a Semi-automatic ICD-10 Coding System

A Methodology for Defining Working Healthcare Ontologies

Infektionskrankheiten und ihre Codierung

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