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Automatic extraction of semantic relations between medical entities: a rule based approach

BACKGROUND: Information extraction is a complex task which is necessary to develop high-precision information retrieval tools. In this paper, we present the platform MeTAE (Medical Texts Annotation and Exploration). MeTAE allows (i) to extract and annotate medical entities and relationships from med...

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Autores principales: Ben Abacha, Asma, Zweigenbaum, Pierre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3239304/
https://www.ncbi.nlm.nih.gov/pubmed/22166723
http://dx.doi.org/10.1186/2041-1480-2-S5-S4
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author Ben Abacha, Asma
Zweigenbaum, Pierre
author_facet Ben Abacha, Asma
Zweigenbaum, Pierre
author_sort Ben Abacha, Asma
collection PubMed
description BACKGROUND: Information extraction is a complex task which is necessary to develop high-precision information retrieval tools. In this paper, we present the platform MeTAE (Medical Texts Annotation and Exploration). MeTAE allows (i) to extract and annotate medical entities and relationships from medical texts and (ii) to explore semantically the produced RDF annotations. RESULTS: Our annotation approach relies on linguistic patterns and domain knowledge and consists in two steps: (i) recognition of medical entities and (ii) identification of the correct semantic relation between each pair of entities. The first step is achieved by an enhanced use of MetaMap which improves the precision obtained by MetaMap by 19.59% in our evaluation. The second step relies on linguistic patterns which are built semi-automatically from a corpus selected according to semantic criteria. We evaluate our system’s ability to identify medical entities of 16 types. We also evaluate the extraction of treatment relations between a treatment (e.g. medication) and a problem (e.g. disease): we obtain 75.72% precision and 60.46% recall. CONCLUSIONS: According to our experiments, using an external sentence segmenter and noun phrase chunker may improve the precision of MetaMap-based medical entity recognition. Our pattern-based relation extraction method obtains good precision and recall w.r.t related works. A more precise comparison with related approaches remains difficult however given the differences in corpora and in the exact nature of the extracted relations. The selection of MEDLINE articles through queries related to known drug-disease pairs enabled us to obtain a more focused corpus of relevant examples of treatment relations than a more general MEDLINE query.
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spelling pubmed-32393042011-12-16 Automatic extraction of semantic relations between medical entities: a rule based approach Ben Abacha, Asma Zweigenbaum, Pierre J Biomed Semantics Research BACKGROUND: Information extraction is a complex task which is necessary to develop high-precision information retrieval tools. In this paper, we present the platform MeTAE (Medical Texts Annotation and Exploration). MeTAE allows (i) to extract and annotate medical entities and relationships from medical texts and (ii) to explore semantically the produced RDF annotations. RESULTS: Our annotation approach relies on linguistic patterns and domain knowledge and consists in two steps: (i) recognition of medical entities and (ii) identification of the correct semantic relation between each pair of entities. The first step is achieved by an enhanced use of MetaMap which improves the precision obtained by MetaMap by 19.59% in our evaluation. The second step relies on linguistic patterns which are built semi-automatically from a corpus selected according to semantic criteria. We evaluate our system’s ability to identify medical entities of 16 types. We also evaluate the extraction of treatment relations between a treatment (e.g. medication) and a problem (e.g. disease): we obtain 75.72% precision and 60.46% recall. CONCLUSIONS: According to our experiments, using an external sentence segmenter and noun phrase chunker may improve the precision of MetaMap-based medical entity recognition. Our pattern-based relation extraction method obtains good precision and recall w.r.t related works. A more precise comparison with related approaches remains difficult however given the differences in corpora and in the exact nature of the extracted relations. The selection of MEDLINE articles through queries related to known drug-disease pairs enabled us to obtain a more focused corpus of relevant examples of treatment relations than a more general MEDLINE query. BioMed Central 2011-10-06 /pmc/articles/PMC3239304/ /pubmed/22166723 http://dx.doi.org/10.1186/2041-1480-2-S5-S4 Text en Copyright ©2011 Ben Abacha and Zweigenbaum; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Ben Abacha, Asma
Zweigenbaum, Pierre
Automatic extraction of semantic relations between medical entities: a rule based approach
title Automatic extraction of semantic relations between medical entities: a rule based approach
title_full Automatic extraction of semantic relations between medical entities: a rule based approach
title_fullStr Automatic extraction of semantic relations between medical entities: a rule based approach
title_full_unstemmed Automatic extraction of semantic relations between medical entities: a rule based approach
title_short Automatic extraction of semantic relations between medical entities: a rule based approach
title_sort automatic extraction of semantic relations between medical entities: a rule based approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3239304/
https://www.ncbi.nlm.nih.gov/pubmed/22166723
http://dx.doi.org/10.1186/2041-1480-2-S5-S4
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