Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT.
An ontology offers a human-readable and machine-computable representation of the concepts in a domain and the relationships among them. Mappings between ontologies enable the reuse and interoperability of biomedical knowledge. We sought to map concepts of the Radiology Gamuts Ontology (RGO), an onto...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 2; pp. 206 - 211 |
|---|---|
| Autores principales: | , |
| Formato: | computer program research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135821497&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135821497 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2019 vid: 32 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135821497 135821497 135821497 10.1007/s10278-019-00186-3 135821497 ppf: 206 ppct: 5 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT. aug: au: Filice, Ross W. Kahn, Charles E. affil: Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA sug: subj: Ontologies Diagnosis, Differential International Classification of Diseases Coding Nomenclature Human Data Mining Electronic Health Records Phenotype Digital Imaging Diagnostic Reasoning ab: An ontology offers a human-readable and machine-computable representation of the concepts in a domain and the relationships among them. Mappings between ontologies enable the reuse and interoperability of biomedical knowledge. We sought to map concepts of the Radiology Gamuts Ontology (RGO), an ontology that links diseases and imaging findings to support differential diagnosis in radiology, to terms in three key vocabularies for clinical radiology: the International Classification of Diseases, version 10, Clinical Modification (ICD-10-CM), the Radiological Society of North America's radiology lexicon (RadLex), and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). RGO (version 0.7; Jan 2018) incorporated 16,918 terms (classes) for diseases, interventions, and imaging observations linked by 1782 subsumption (class-subclass) relations and 55,569 causal ("may cause") relations. RGO classes were mapped to RadLex (46,656 classes, version 3.15), SNOMED CT (347,358 classes, version 2018AA), and ICD-10-CM (94,645 classes, version 2018AA) using the National Center for Biomedical Ontology (NCBO) Annotator web service. We identified 1275 exact mappings from RGO to RadLex, 5302 to SNOMED CT, and 941 to ICD-10-CM. RGO terms mapped to one ontology (n = 3401), two ontologies (n = 1515), or all three ontologies (n = 198). The mapped ontologies provide additional terms to support data mining from textual information in the electronic health record. The current work builds on efforts to map RGO to ontologies of diseases and phenotypes. Mappings between ontologies can support automated knowledge discovery, diagnostic reasoning, and data mining. pubtype: Academic Journal doctype: computer program research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|