Linking patient data to scientific knowledge to support contextualized mining

Detalhes bibliográficos
Autor(a) principal: Carvalho, Ricardo Miguel Serafim de
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/53897
Resumo: Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2022
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spelling Linking patient data to scientific knowledge to support contextualized miningAnotação semanticaOntologias BiomédicasReadmissões em UCIApredizagme automáticaEmbedding de grafos de conhecimentoTeses de mestrado - 2022Departamento de InformáticaTese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2022ICU readmissions are a critical problem associated with either serious conditions, ill nesses, or complications, representing a 4 times increase in mortality risk and a financial burden to health institutions. In developed countries 1 in every 10 patients discharged comes back to the ICU. As hospitals become more and more data-oriented with the adop tion of Electronic Health Records (EHR), there as been a rise in the development of com putational approaches to support clinical decision. In recent years new efforts emerged, using machine learning approaches to make ICU readmission predictions directly over EHR data. Despite these growing efforts, machine learning approaches still explore EHR data directly without taking into account its mean ing or context. Medical knowledge is not accessible to these methods, who work blindly over the data, without considering the meaning and relationships the data objects. Ontolo gies and knowledge graphs can help bridge this gap between data and scientific context, since they are computational artefacts that represent the entities in a domain and how the relate to each other in a formalized fashion. This opportunity motivated the aim of this work: to investigate how enriching EHR data with ontology-based semantic annotations and applying machine learning techniques that explore them can impact the prediction of 30-day ICU readmission risk. To achieve this, a number of contributions were developed, including: (1) An enrichment of the MIMIC-III data set with annotations to several biomedical ontologies; (2) A novel ap proach to predict ICU readmission risk that explores knowledge graph embeddings to represent patient data taking into account the semantic annotations; (3) A variant of the predictive approach that targets different moments to support risk prediction throughout the ICU stay. The predictive approaches outperformed both state-of-the-art and a baseline achieving a ROC-AUC of 0.815 (an increase of 0.2 over the state of the art). The positive results achieved motivated the development of an entrepreneurial project, which placed in the Top 5 of the H-INNOVA 2021 entrepreneurship award.Pesquita, Cátia, 1980-Oliveira, Daniela Patrícia dos SantosRepositório da Universidade de LisboaCarvalho, Ricardo Miguel Serafim de2022-07-21T09:05:38Z202220222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/53897enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:00:08Zoai:repositorio.ul.pt:10451/53897Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:04:53.036845Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Linking patient data to scientific knowledge to support contextualized mining
title Linking patient data to scientific knowledge to support contextualized mining
spellingShingle Linking patient data to scientific knowledge to support contextualized mining
Carvalho, Ricardo Miguel Serafim de
Anotação semantica
Ontologias Biomédicas
Readmissões em UCI
Apredizagme automática
Embedding de grafos de conhecimento
Teses de mestrado - 2022
Departamento de Informática
title_short Linking patient data to scientific knowledge to support contextualized mining
title_full Linking patient data to scientific knowledge to support contextualized mining
title_fullStr Linking patient data to scientific knowledge to support contextualized mining
title_full_unstemmed Linking patient data to scientific knowledge to support contextualized mining
title_sort Linking patient data to scientific knowledge to support contextualized mining
author Carvalho, Ricardo Miguel Serafim de
author_facet Carvalho, Ricardo Miguel Serafim de
author_role author
dc.contributor.none.fl_str_mv Pesquita, Cátia, 1980-
Oliveira, Daniela Patrícia dos Santos
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Carvalho, Ricardo Miguel Serafim de
dc.subject.por.fl_str_mv Anotação semantica
Ontologias Biomédicas
Readmissões em UCI
Apredizagme automática
Embedding de grafos de conhecimento
Teses de mestrado - 2022
Departamento de Informática
topic Anotação semantica
Ontologias Biomédicas
Readmissões em UCI
Apredizagme automática
Embedding de grafos de conhecimento
Teses de mestrado - 2022
Departamento de Informática
description Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2022
publishDate 2022
dc.date.none.fl_str_mv 2022-07-21T09:05:38Z
2022
2022
2022-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/53897
url http://hdl.handle.net/10451/53897
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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