Prediction of Antimicrobial Resistance for Personalized Prevention and Clinical Management of Infectious Diseases

Detalhes bibliográficos
Autor(a) principal: Braz, Nuno Gonçalo Viegas Ludovico
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/58971
Resumo: Tese de mestrado, Ciência de Dados, 2023, Universidade de Lisboa, Faculdade de Ciências
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spelling Prediction of Antimicrobial Resistance for Personalized Prevention and Clinical Management of Infectious DiseasesResistência a AntibióticosModelação PreditivaPrevisão de Resistência a AntibióticosPrevisão de Infeções NosocomiaisAnálise Económica de Modelos PreditivosTeses de mestrado - 2023Departamento de InformáticaTese de mestrado, Ciência de Dados, 2023, Universidade de Lisboa, Faculdade de CiênciasAntibiotics are a very important class of drugs in modern Medicine; its discovery and introduction constituted a major revolution in Medicine. As such, the decrease of their effectiveness due to the rising levels of antibiotic resistance is a great concern to our society. Hospital-Acquired Infections (HAI), also known as nosocomial infections, are infections that a patient acquires in the context of medical treatments. Nosocomial infections are closely related to the problem of resistance to antibiotics, because a large part of these infections are caused by antibiotic resistant bacteria. This aspect motivates the tackling of these two problems jointly. This work was performed in the frame of the RESISTIR project, that aims to develop a Decision Support System for intelligent control of infection and personalized antibiotherapy. The data was collected by a portuguese software company, Maxdata Software, S.A.; then, in the scope of this project, it was pre-processed and inserted into a database. The database contains information generated by clinical episodes with origin in three portuguese hospitals, dated from 2013 to 2016. The available data allowed us to develop predictive models of risk of antibiotic resistance (AMR models) and risk of nosocomial infection (HAI model). For the AMR models, we aggregated the antibiotics into the level 4 of the ATC classification system, and focused on four classes of antibiotics: J01MA (fluoroquinolones), J01CA (penicillins with extended spectrum), J01DC (second-generation cephalosporins) and J01DH (carbapenems). In all predictive models developed, the features generated related to the clinical history of the patients and the health units involved in the episodes were found to be the most significant. We concluded that the main goals of this project were achieved, with the development of prototype predictive models of risk of antibiotic resistance and risk of nosocomial infection. The predictive power of the models, as measured by the ROC-AUC, ranged from 0.720 to 0.857 for the AMR models, and was 0.915 for the HAI model. These prototype predictive models that we were able to develop show that it is possible to deploy in healthcare units a Decision Support System that can help to monitor and reduce the problem of resistance to antibiotics and the nosocomial infections. For the HAI predictive model developed, we made an estimate of its benefits if it would be deployed in production in the three hospitals involved in this project. We drew the conclusion that the following savings would be obtained, in 2007 euros and considering only the hospitalizations: about 20 million euros and a reduction of approximately 60 000 days in the hospital stays, per year.Dias, Ricardo Pedro MoreiraRepositório da Universidade de LisboaBraz, Nuno Gonçalo Viegas Ludovico202320232026-02-27T00:00:00Z2023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/58971enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:07:56Zoai:repositorio.ul.pt:10451/58971Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:09:01.961905Repositório 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 Prediction of Antimicrobial Resistance for Personalized Prevention and Clinical Management of Infectious Diseases
title Prediction of Antimicrobial Resistance for Personalized Prevention and Clinical Management of Infectious Diseases
spellingShingle Prediction of Antimicrobial Resistance for Personalized Prevention and Clinical Management of Infectious Diseases
Braz, Nuno Gonçalo Viegas Ludovico
Resistência a Antibióticos
Modelação Preditiva
Previsão de Resistência a Antibióticos
Previsão de Infeções Nosocomiais
Análise Económica de Modelos Preditivos
Teses de mestrado - 2023
Departamento de Informática
title_short Prediction of Antimicrobial Resistance for Personalized Prevention and Clinical Management of Infectious Diseases
title_full Prediction of Antimicrobial Resistance for Personalized Prevention and Clinical Management of Infectious Diseases
title_fullStr Prediction of Antimicrobial Resistance for Personalized Prevention and Clinical Management of Infectious Diseases
title_full_unstemmed Prediction of Antimicrobial Resistance for Personalized Prevention and Clinical Management of Infectious Diseases
title_sort Prediction of Antimicrobial Resistance for Personalized Prevention and Clinical Management of Infectious Diseases
author Braz, Nuno Gonçalo Viegas Ludovico
author_facet Braz, Nuno Gonçalo Viegas Ludovico
author_role author
dc.contributor.none.fl_str_mv Dias, Ricardo Pedro Moreira
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Braz, Nuno Gonçalo Viegas Ludovico
dc.subject.por.fl_str_mv Resistência a Antibióticos
Modelação Preditiva
Previsão de Resistência a Antibióticos
Previsão de Infeções Nosocomiais
Análise Económica de Modelos Preditivos
Teses de mestrado - 2023
Departamento de Informática
topic Resistência a Antibióticos
Modelação Preditiva
Previsão de Resistência a Antibióticos
Previsão de Infeções Nosocomiais
Análise Económica de Modelos Preditivos
Teses de mestrado - 2023
Departamento de Informática
description Tese de mestrado, Ciência de Dados, 2023, Universidade de Lisboa, Faculdade de Ciências
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023-01-01T00:00:00Z
2026-02-27T00:00:00Z
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/10451/58971
url http://hdl.handle.net/10451/58971
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
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
repository.mail.fl_str_mv
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