Automatic concept extraction from biomedical material

Treball fi de màster de: Master in Intelligent Interactive Systems

Detalles Bibliográficos
Autor: Marin, Albert
Tipo de recurso: tesis de maestría
Fecha de publicación:2018
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/35699
Acceso en línea:http://hdl.handle.net/10230/35699
Access Level:acceso abierto
Palabra clave:Tractament del llenguatge natural (Informàtica)
Natural Language Processing (NLP)
Named Entity Recognition (NER)
Biomedicine
Deep learning
Transfer learning
Intervertebral discs
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spelling Automatic concept extraction from biomedical materialMarin, AlbertTractament del llenguatge natural (Informàtica)Natural Language Processing (NLP)Named Entity Recognition (NER)BiomedicineDeep learningTransfer learningIntervertebral discsTreball fi de màster de: Master in Intelligent Interactive SystemsTutors: Leo Wanner, Jérôme NoaillyNatural Language Processing is a vibrant field of computer science that provides computers with the ability of understanding human language. In the field of medical data, there is a demanding need to lower the amount of documents clinicians and researchers need to manage in order to learn new concepts to improve their day-today practice. The research presented in this thesis aims at the design and evaluation of an algorithm based on neural networks that will extract the relevant entities from biomedical papers in order to reduce the amount of time needed for reading papers. Of all the topics in medicine that can take advantage of this thesis, the one it has been chosen in particular is the one of intervertebral discs. One of the reasons is the availability of experts on the topic in the current university. Moreover, it is a very interesting field as cells that form part of this structure have different properties based on their location. This makes it indeed a complex task to retrieve the relevant information because depending on the considered region some properties will be prominent whereas in other they might not be that relevant. The methodology used in the process it has been to use some off-the-shelf libraries already implemented in Java as a baseline and then use python to code a new architecture modifications to allow the algorithm to detect the relevant named entities. The results are compared with the gold standard obtained from the experts in the field and the conclusions are drawn from the observations.Financial support for the work of this thesis was received from the María de Maeztu Units of Excellence Program MDM-2015-0502 and from the Chair QUAES-UPF Computational Technologies for Healthcare.201820182018info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/35699reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/356992026-05-29T05:05:01Z
dc.title.none.fl_str_mv Automatic concept extraction from biomedical material
title Automatic concept extraction from biomedical material
spellingShingle Automatic concept extraction from biomedical material
Marin, Albert
Tractament del llenguatge natural (Informàtica)
Natural Language Processing (NLP)
Named Entity Recognition (NER)
Biomedicine
Deep learning
Transfer learning
Intervertebral discs
title_short Automatic concept extraction from biomedical material
title_full Automatic concept extraction from biomedical material
title_fullStr Automatic concept extraction from biomedical material
title_full_unstemmed Automatic concept extraction from biomedical material
title_sort Automatic concept extraction from biomedical material
dc.creator.none.fl_str_mv Marin, Albert
author Marin, Albert
author_facet Marin, Albert
author_role author
dc.subject.none.fl_str_mv Tractament del llenguatge natural (Informàtica)
Natural Language Processing (NLP)
Named Entity Recognition (NER)
Biomedicine
Deep learning
Transfer learning
Intervertebral discs
topic Tractament del llenguatge natural (Informàtica)
Natural Language Processing (NLP)
Named Entity Recognition (NER)
Biomedicine
Deep learning
Transfer learning
Intervertebral discs
description Treball fi de màster de: Master in Intelligent Interactive Systems
publishDate 2018
dc.date.none.fl_str_mv 2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/35699
url http://hdl.handle.net/10230/35699
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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