Abstract:
: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.
journal_name
J Digit Imagingjournal_title
Journal of digital imagingauthors
Filice RW,Kahn CE Jrdoi
10.1007/s10278-019-00186-3subject
Has Abstractpub_date
2019-04-01 00:00:00pages
206-210issue
2eissn
0897-1889issn
1618-727Xpii
10.1007/s10278-019-00186-3journal_volume
32pub_type
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journal_title:Journal of digital imaging
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journal_title:Journal of digital imaging
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journal_title:Journal of digital imaging
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journal_title:Journal of digital imaging
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journal_title:Journal of digital imaging
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doi:10.1007/s10278-006-0925-9
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journal_title:Journal of digital imaging
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更新日期:2001-03-01 00:00:00
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journal_title:Journal of digital imaging
pub_type: 杂志文章
doi:10.1007/BF03190351
更新日期:2001-06-01 00:00:00
abstract::Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of tra...
journal_title:Journal of digital imaging
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doi:10.1007/s10278-019-00196-1
更新日期:2019-12-01 00:00:00
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journal_title:Journal of digital imaging
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更新日期:2019-04-01 00:00:00
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journal_title:Journal of digital imaging
pub_type: 杂志文章,评审
doi:10.1007/s10278-007-9081-0
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更新日期:1990-08-01 00:00:00
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journal_title:Journal of digital imaging
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doi:10.1007/BF03167622
更新日期:2000-05-01 00:00:00
abstract::In this paper, a simplified yet efficient architecture of a deep convolutional neural network is presented for lung image classification. The images used for classification are computed tomography (CT) scan images obtained from two scientifically used databases available publicly. Six external shape-based features, vi...
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doi:10.1007/s10278-019-00245-9
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journal_title:Journal of digital imaging
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journal_title:Journal of digital imaging
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doi:10.1007/s10278-006-0773-7
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journal_title:Journal of digital imaging
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