Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine

Detalhes bibliográficos
Autor(a) principal: Gonçalves, João
Data de Publicação: 2014
Outros Autores: Portela, Filipe, Santos, Manuel Filipe, Silva, Álvaro, Machado, José Manuel, Abelha, António, Rua, Fernando
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/30785
Resumo: "Accepted for publication"
id RCAP_13e4ac8dc6e2cdfa6ed96aaca78e063b
oai_identifier_str oai:repositorium.sdum.uminho.pt:1822/30785
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str
spelling Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicineData MiningClassificationIntensive CareSepsisPredict Therapeutic PlansIntcareClassification modelsINTCare projectSepsis levelTherapeutic plansScience & Technology"Accepted for publication"This work aims to support doctor’s decision-making on predicting sepsis level and the best treatment for patients with microbiological problems. A set of Data Mining (DM) models was developed using forecasting techniques and classification models which will enable doctors’ decisions about the appropriate therapy to apply, as well as the most successful one. The data used in DM models were collected at the Intensive Care Unit (ICU) of the Centro Hospitalar do Porto, in Oporto, Portugal. Classification models where considered to predict sepsis level and therapeutic plan for patients with sepsis in a supervised learning approach. Models were induced making use of the following algorithms: Decision Trees, Support Vector Machines and Naïve Bayes classifier. Confusion Matrix, including associated metrics, and Cross-validation were used for the evaluation. Analysis of the total error rate, sensitivity, specificity and accuracy were the associated metrics used to identify the most relevant measures to predict sepsis level and treatment plan under study. In conclusion, it was possible to predict with great accuracy the sepsis level (2nd and 3rd), but not the therapeutic plan. Although the good results attained for sepsis (accuracy: 100%), therapeutic plan does not present the same level of accuracy (best: 62.8%).FCT -Fundação para a Ciência e a Tecnologia(PEst-OE/EEI/UI0319/2014)IGI GlobalUniversidade do MinhoGonçalves, JoãoPortela, FilipeSantos, Manuel FilipeSilva, ÁlvaroMachado, José ManuelAbelha, AntónioRua, Fernando2014-11-062014-11-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/30785eng1555-339610.4018/ijhisi.2014070103info: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:RCAAP2023-07-21T12:20:46ZPortal AgregadorONG
dc.title.none.fl_str_mv Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine
title Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine
spellingShingle Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine
Gonçalves, João
Data Mining
Classification
Intensive Care
Sepsis
Predict Therapeutic Plans
Intcare
Classification models
INTCare project
Sepsis level
Therapeutic plans
Science & Technology
title_short Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine
title_full Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine
title_fullStr Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine
title_full_unstemmed Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine
title_sort Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine
author Gonçalves, João
author_facet Gonçalves, João
Portela, Filipe
Santos, Manuel Filipe
Silva, Álvaro
Machado, José Manuel
Abelha, António
Rua, Fernando
author_role author
author2 Portela, Filipe
Santos, Manuel Filipe
Silva, Álvaro
Machado, José Manuel
Abelha, António
Rua, Fernando
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Gonçalves, João
Portela, Filipe
Santos, Manuel Filipe
Silva, Álvaro
Machado, José Manuel
Abelha, António
Rua, Fernando
dc.subject.por.fl_str_mv Data Mining
Classification
Intensive Care
Sepsis
Predict Therapeutic Plans
Intcare
Classification models
INTCare project
Sepsis level
Therapeutic plans
Science & Technology
topic Data Mining
Classification
Intensive Care
Sepsis
Predict Therapeutic Plans
Intcare
Classification models
INTCare project
Sepsis level
Therapeutic plans
Science & Technology
description "Accepted for publication"
publishDate 2014
dc.date.none.fl_str_mv 2014-11-06
2014-11-06T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/30785
url http://hdl.handle.net/1822/30785
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1555-3396
10.4018/ijhisi.2014070103
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.publisher.none.fl_str_mv IGI Global
publisher.none.fl_str_mv IGI Global
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
repository.mail.fl_str_mv
_version_ 1777303735317299200

Registros relacionados