Literature DB >> 24428295

Informatics in radiology: radiology gamuts ontology: differential diagnosis for the Semantic Web.

Joseph J Budovec1, Cesar A Lam, Charles E Kahn.   

Abstract

The Semantic Web is an effort to add semantics, or "meaning," to empower automated searching and processing of Web-based information. The overarching goal of the Semantic Web is to enable users to more easily find, share, and combine information. Critical to this vision are knowledge models called ontologies, which define a set of concepts and formalize the relations between them. Ontologies have been developed to manage and exploit the large and rapidly growing volume of information in biomedical domains. In diagnostic radiology, lists of differential diagnoses of imaging observations, called gamuts, provide an important source of knowledge. The Radiology Gamuts Ontology (RGO) is a formal knowledge model of differential diagnoses in radiology that includes 1674 differential diagnoses, 19,017 terms, and 52,976 links between terms. Its knowledge is used to provide an interactive, freely available online reference of radiology gamuts ( www.gamuts.net ). A Web service allows its content to be discovered and consumed by other information systems. The RGO integrates radiologic knowledge with other biomedical ontologies as part of the Semantic Web. © RSNA, 2014.

Mesh:

Year:  2013        PMID: 24428295     DOI: 10.1148/rg.341135036

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  9 in total

1.  Will Artificial Intelligence Replace Radiologists?

Authors:  Curtis P Langlotz
Journal:  Radiol Artif Intell       Date:  2019-05-15

2.  Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT.

Authors:  Ross W Filice; Charles E Kahn
Journal:  J Digit Imaging       Date:  2019-04       Impact factor: 4.056

3.  Imaging Informatics: 25 Years of Progress.

Authors:  J P Agrawal; B J Erickson; C E Kahn
Journal:  Yearb Med Inform       Date:  2016-06-30

Review 4.  The Representation of Causality and Causation with Ontologies: A Systematic Literature Review.

Authors:  Suhila Sawesi; Mohamed Rashrash; Olaf Dammann
Journal:  Online J Public Health Inform       Date:  2022-09-07

5.  Biomedical imaging ontologies: A survey and proposal for future work.

Authors:  Barry Smith; Sivaram Arabandi; Mathias Brochhausen; Michael Calhoun; Paolo Ciccarese; Scott Doyle; Bernard Gibaud; Ilya Goldberg; Charles E Kahn; James Overton; John Tomaszewski; Metin Gurcan
Journal:  J Pathol Inform       Date:  2015-06-23

6.  Developing a knowledge base to support the annotation of ultrasound images of ectopic pregnancy.

Authors:  Ferdinand Dhombres; Paul Maurice; Stéphanie Friszer; Lucie Guilbaud; Nathalie Lelong; Babak Khoshnood; Jean Charlet; Nicolas Perrot; Eric Jauniaux; Davor Jurkovic; Jean-Marie Jouannic
Journal:  J Biomed Semantics       Date:  2017-01-31

Review 7.  Standard Lexicons, Coding Systems and Ontologies for Interoperability and Semantic Computation in Imaging.

Authors:  Kenneth C Wang
Journal:  J Digit Imaging       Date:  2018-06       Impact factor: 4.056

8.  Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis.

Authors:  Pavithra I Dissanayake; Tiago K Colicchio; James J Cimino
Journal:  J Am Med Inform Assoc       Date:  2020-01-01       Impact factor: 4.497

Review 9.  Biomedical Ontologies to Guide AI Development in Radiology.

Authors:  Ross W Filice; Charles E Kahn
Journal:  J Digit Imaging       Date:  2021-11-01       Impact factor: 4.903

  9 in total

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