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Semantator: Annotating Clinical Narratives with Semantic Web Ontologies

To facilitate clinical research, clinical data needs to be stored in a machine processable and understandable way. Manual annotating clinical data is time consuming. Automatic approaches (e.g., Natural Language Processing systems) have been adopted to convert such data into structured formats; howev...

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Detalles Bibliográficos
Autores principales: Song, Dezhao, Chute, Christopher G., Tao, Cui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392053/
https://www.ncbi.nlm.nih.gov/pubmed/22779043
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author Song, Dezhao
Chute, Christopher G.
Tao, Cui
author_facet Song, Dezhao
Chute, Christopher G.
Tao, Cui
author_sort Song, Dezhao
collection PubMed
description To facilitate clinical research, clinical data needs to be stored in a machine processable and understandable way. Manual annotating clinical data is time consuming. Automatic approaches (e.g., Natural Language Processing systems) have been adopted to convert such data into structured formats; however, the quality of such automatically extracted data may not always be satisfying. In this paper, we propose Semantator, a semi-automatic tool for document annotation with Semantic Web ontologies. With a loaded free text document and an ontology, Semantator supports the creation/deletion of ontology instances for any document fragment, linking/disconnecting instances with the properties in the ontology, and also enables automatic annotation by connecting to the NCBO annotator and cTAKES. By representing annotations in Semantic Web standards, Semantator supports reasoning based upon the underlying semantics of the owl:disjointWith and owl:equivalentClass predicates. We present discussions based on user experiences of using Semantator.
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spelling pubmed-33920532012-07-09 Semantator: Annotating Clinical Narratives with Semantic Web Ontologies Song, Dezhao Chute, Christopher G. Tao, Cui AMIA Jt Summits Transl Sci Proc Articles To facilitate clinical research, clinical data needs to be stored in a machine processable and understandable way. Manual annotating clinical data is time consuming. Automatic approaches (e.g., Natural Language Processing systems) have been adopted to convert such data into structured formats; however, the quality of such automatically extracted data may not always be satisfying. In this paper, we propose Semantator, a semi-automatic tool for document annotation with Semantic Web ontologies. With a loaded free text document and an ontology, Semantator supports the creation/deletion of ontology instances for any document fragment, linking/disconnecting instances with the properties in the ontology, and also enables automatic annotation by connecting to the NCBO annotator and cTAKES. By representing annotations in Semantic Web standards, Semantator supports reasoning based upon the underlying semantics of the owl:disjointWith and owl:equivalentClass predicates. We present discussions based on user experiences of using Semantator. American Medical Informatics Association 2012-03-19 /pmc/articles/PMC3392053/ /pubmed/22779043 Text en ©2012 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
spellingShingle Articles
Song, Dezhao
Chute, Christopher G.
Tao, Cui
Semantator: Annotating Clinical Narratives with Semantic Web Ontologies
title Semantator: Annotating Clinical Narratives with Semantic Web Ontologies
title_full Semantator: Annotating Clinical Narratives with Semantic Web Ontologies
title_fullStr Semantator: Annotating Clinical Narratives with Semantic Web Ontologies
title_full_unstemmed Semantator: Annotating Clinical Narratives with Semantic Web Ontologies
title_short Semantator: Annotating Clinical Narratives with Semantic Web Ontologies
title_sort semantator: annotating clinical narratives with semantic web ontologies
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392053/
https://www.ncbi.nlm.nih.gov/pubmed/22779043
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