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SNOMEDtxt: Natural Language Generation from SNOMED Ontology

SNOMED Clinical Terms (SNOMED CT) defines over 70,000 diseases, including many rare ones. Meanwhile, descriptions of rare conditions are missing from online educational resources. SNOMEDtxt converts ontological concept definitions and relations contained in SNOMED CT into narrative disease descripti...

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Detalles Bibliográficos
Autores principales: Lyudovyk, Olga, Weng, Chunhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6852688/
https://www.ncbi.nlm.nih.gov/pubmed/31438128
http://dx.doi.org/10.3233/SHTI190429
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author Lyudovyk, Olga
Weng, Chunhua
author_facet Lyudovyk, Olga
Weng, Chunhua
author_sort Lyudovyk, Olga
collection PubMed
description SNOMED Clinical Terms (SNOMED CT) defines over 70,000 diseases, including many rare ones. Meanwhile, descriptions of rare conditions are missing from online educational resources. SNOMEDtxt converts ontological concept definitions and relations contained in SNOMED CT into narrative disease descriptions using Natural Language Generation techniques. Generated text is evaluated using both computational methods and clinician and lay user feedback. User evaluations indicate that lay people prefer generated text to the original SNOMED content, find it more informative, and understand it significantly better. This method promises to improve access to clinical knowledge for patients and the medical community and to assist in ontology auditing through natural language descriptions.
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spelling pubmed-68526882019-11-13 SNOMEDtxt: Natural Language Generation from SNOMED Ontology Lyudovyk, Olga Weng, Chunhua Stud Health Technol Inform Article SNOMED Clinical Terms (SNOMED CT) defines over 70,000 diseases, including many rare ones. Meanwhile, descriptions of rare conditions are missing from online educational resources. SNOMEDtxt converts ontological concept definitions and relations contained in SNOMED CT into narrative disease descriptions using Natural Language Generation techniques. Generated text is evaluated using both computational methods and clinician and lay user feedback. User evaluations indicate that lay people prefer generated text to the original SNOMED content, find it more informative, and understand it significantly better. This method promises to improve access to clinical knowledge for patients and the medical community and to assist in ontology auditing through natural language descriptions. 2019-08-21 /pmc/articles/PMC6852688/ /pubmed/31438128 http://dx.doi.org/10.3233/SHTI190429 Text en http://creativecommons.org/licenses/by/4.0/ This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
spellingShingle Article
Lyudovyk, Olga
Weng, Chunhua
SNOMEDtxt: Natural Language Generation from SNOMED Ontology
title SNOMEDtxt: Natural Language Generation from SNOMED Ontology
title_full SNOMEDtxt: Natural Language Generation from SNOMED Ontology
title_fullStr SNOMEDtxt: Natural Language Generation from SNOMED Ontology
title_full_unstemmed SNOMEDtxt: Natural Language Generation from SNOMED Ontology
title_short SNOMEDtxt: Natural Language Generation from SNOMED Ontology
title_sort snomedtxt: natural language generation from snomed ontology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6852688/
https://www.ncbi.nlm.nih.gov/pubmed/31438128
http://dx.doi.org/10.3233/SHTI190429
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