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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2019
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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. |
format | Online Article Text |
id | pubmed-6852688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
record_format | MEDLINE/PubMed |
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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