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

Overview of attention for article published in Journal of Biomedical Semantics, October 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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1 patent

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37 Mendeley
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Title
Dione: An OWL representation of ICD-10-CM for classifying patients’ diseases
Published in
Journal of Biomedical Semantics, October 2016
DOI 10.1186/s13326-016-0105-x
Pubmed ID
Authors

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

Abstract

Systematized 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. Dione 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. Dione 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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 3%
United States 1 3%
Unknown 35 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 16%
Researcher 6 16%
Student > Doctoral Student 3 8%
Professor 2 5%
Student > Bachelor 2 5%
Other 9 24%
Unknown 9 24%
Readers by discipline Count As %
Computer Science 10 27%
Agricultural and Biological Sciences 6 16%
Engineering 5 14%
Medicine and Dentistry 5 14%
Unspecified 1 3%
Other 3 8%
Unknown 7 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 August 2021.
All research outputs
#5,438,883
of 22,893,031 outputs
Outputs from Journal of Biomedical Semantics
#77
of 364 outputs
Outputs of similar age
#82,898
of 319,475 outputs
Outputs of similar age from Journal of Biomedical Semantics
#4
of 9 outputs
Altmetric has tracked 22,893,031 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 319,475 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.