Contributions to information extraction for spanish written biomedical text

285 p.

Detalles Bibliográficos
Autor: Pérez Miguel, Naiara
Tipo de recurso: tesis doctoral
Fecha de publicación:2023
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/61337
Acceso en línea:http://hdl.handle.net/10810/61337
Access Level:acceso abierto
Palabra clave:artificial intelligence
informatics
computational linguistics
inteligencia artificial
informática
lingüística computacional
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network_acronym_str ES
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repository_id_str
spelling Contributions to information extraction for spanish written biomedical textPérez Miguel, Naiaraartificial intelligenceinformaticscomputational linguisticsinteligencia artificialinformáticalingüística computacional285 p.Healthcare practice and clinical research produce vast amounts of digitised, unstructured data in multiple languages that are currently underexploited, despite their potential applications in improving healthcare experiences, supporting trainee education, or enabling biomedical research, for example. To automatically transform those contents into relevant, structured information, advanced Natural Language Processing (NLP) mechanisms are required. In NLP, this task is known as Information Extraction. Our work takes place within this growing field of clinical NLP for the Spanish language, as we tackle three distinct problems. First, we compare several supervised machine learning approaches to the problem of sensitive data detection and classification. Specifically, we study the different approaches and their transferability in two corpora, one synthetic and the other authentic. Second, we present and evaluate UMLSmapper, a knowledge-intensive system for biomedical term identification based on the UMLS Metathesaurus. This system recognises and codifies terms without relying on annotated data nor external Named Entity Recognition tools. Although technically naive, it performs on par with more evolved systems, and does not exhibit a considerable deviation from other approaches that rely on oracle terms. Finally, we present and exploit a new corpus of real health records manually annotated with negation and uncertainty information: NUBes. This corpus is the basis for two sets of experiments, one on cue andscope detection, and the other on assertion classification. Throughout the thesis, we apply and compare techniques of varying levels of sophistication and novelty, which reflects the rapid advancement of the field.Rigau Claramunt, GermánCuadros Oller, Montserrat2023202320232023info:eu-repo/semantics/doctoralThesisapplication/pdfhttp://hdl.handle.net/10810/61337reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/es/Atribución-NoComercial 3.0 España(cc)2023 NAIARA PEREZ MIGUEL (cc by-nc 4.0)oai:addi.ehu.eus:10810/613372026-06-18T09:23:17Z
dc.title.none.fl_str_mv Contributions to information extraction for spanish written biomedical text
title Contributions to information extraction for spanish written biomedical text
spellingShingle Contributions to information extraction for spanish written biomedical text
Pérez Miguel, Naiara
artificial intelligence
informatics
computational linguistics
inteligencia artificial
informática
lingüística computacional
title_short Contributions to information extraction for spanish written biomedical text
title_full Contributions to information extraction for spanish written biomedical text
title_fullStr Contributions to information extraction for spanish written biomedical text
title_full_unstemmed Contributions to information extraction for spanish written biomedical text
title_sort Contributions to information extraction for spanish written biomedical text
dc.creator.none.fl_str_mv Pérez Miguel, Naiara
author Pérez Miguel, Naiara
author_facet Pérez Miguel, Naiara
author_role author
dc.contributor.none.fl_str_mv Rigau Claramunt, Germán
Cuadros Oller, Montserrat
dc.subject.none.fl_str_mv artificial intelligence
informatics
computational linguistics
inteligencia artificial
informática
lingüística computacional
topic artificial intelligence
informatics
computational linguistics
inteligencia artificial
informática
lingüística computacional
description 285 p.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/61337
url http://hdl.handle.net/10810/61337
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/es/
Atribución-NoComercial 3.0 España
(cc)2023 NAIARA PEREZ MIGUEL (cc by-nc 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/es/
Atribución-NoComercial 3.0 España
(cc)2023 NAIARA PEREZ MIGUEL (cc by-nc 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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