Survivability prediction of colorectal cancer patients: a system with evolving features for continuous improvement

Detalhes bibliográficos
Autor(a) principal: Oliveira, Tiago
Data de Publicação: 2018
Outros Autores: Silva, Ana, Satoh, Ken, Julian, Vicente, Teixeira, Pedro Alexandre Leão Araújo Gonçalves, Novais, Paulo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/60530
Resumo: Prediction in health care is closely related with the decision-making process. On the one hand, accurate survivability prediction can help physicians decide between palliative care or other practice for a patient. On the other hand, the notion of remaining lifetime can be an incentive for patients to live a fuller and more fulfilling life. This work presents a pipeline for the development of survivability prediction models and a system that provides survivability predictions for years one to five after the treatment of patients with colon or rectal cancer. The functionalities of the system are made available through a tool that balances the number of necessary inputs and prediction performance. It is mobile-friendly and facilitates the access of health care professionals to an instrument capable of enriching their practice and improving outcomes. The performance of survivability models was compared with other existing works in the literature and found to be an improvement over the current state of the art. The underlying system is capable of recalculating its prediction models upon the addition of new data, continuously evolving as time passes.
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spelling Survivability prediction of colorectal cancer patients: a system with evolving features for continuous improvementsurvivability predictionclinical decision supportmachine learningScience & TechnologyPrediction in health care is closely related with the decision-making process. On the one hand, accurate survivability prediction can help physicians decide between palliative care or other practice for a patient. On the other hand, the notion of remaining lifetime can be an incentive for patients to live a fuller and more fulfilling life. This work presents a pipeline for the development of survivability prediction models and a system that provides survivability predictions for years one to five after the treatment of patients with colon or rectal cancer. The functionalities of the system are made available through a tool that balances the number of necessary inputs and prediction performance. It is mobile-friendly and facilitates the access of health care professionals to an instrument capable of enriching their practice and improving outcomes. The performance of survivability models was compared with other existing works in the literature and found to be an improvement over the current state of the art. The underlying system is capable of recalculating its prediction models upon the addition of new data, continuously evolving as time passes.The work of Tiago Oliveira was supported by JSPS KAKENHI Grant Number JP18K18115.info:eu-repo/semantics/publishedVersionMultidisciplinary Digital Publishing InstituteUniversidade do MinhoOliveira, TiagoSilva, AnaSatoh, KenJulian, VicenteTeixeira, Pedro Alexandre Leão Araújo GonçalvesNovais, Paulo2018-09-062018-09-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/60530eng1424-822010.3390/s1809298330200676info: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:RCAAP2023-07-21T12:12:42Zoai:repositorium.sdum.uminho.pt:1822/60530Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:04:39.303393Repositório 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 Survivability prediction of colorectal cancer patients: a system with evolving features for continuous improvement
title Survivability prediction of colorectal cancer patients: a system with evolving features for continuous improvement
spellingShingle Survivability prediction of colorectal cancer patients: a system with evolving features for continuous improvement
Oliveira, Tiago
survivability prediction
clinical decision support
machine learning
Science & Technology
title_short Survivability prediction of colorectal cancer patients: a system with evolving features for continuous improvement
title_full Survivability prediction of colorectal cancer patients: a system with evolving features for continuous improvement
title_fullStr Survivability prediction of colorectal cancer patients: a system with evolving features for continuous improvement
title_full_unstemmed Survivability prediction of colorectal cancer patients: a system with evolving features for continuous improvement
title_sort Survivability prediction of colorectal cancer patients: a system with evolving features for continuous improvement
author Oliveira, Tiago
author_facet Oliveira, Tiago
Silva, Ana
Satoh, Ken
Julian, Vicente
Teixeira, Pedro Alexandre Leão Araújo Gonçalves
Novais, Paulo
author_role author
author2 Silva, Ana
Satoh, Ken
Julian, Vicente
Teixeira, Pedro Alexandre Leão Araújo Gonçalves
Novais, Paulo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Oliveira, Tiago
Silva, Ana
Satoh, Ken
Julian, Vicente
Teixeira, Pedro Alexandre Leão Araújo Gonçalves
Novais, Paulo
dc.subject.por.fl_str_mv survivability prediction
clinical decision support
machine learning
Science & Technology
topic survivability prediction
clinical decision support
machine learning
Science & Technology
description Prediction in health care is closely related with the decision-making process. On the one hand, accurate survivability prediction can help physicians decide between palliative care or other practice for a patient. On the other hand, the notion of remaining lifetime can be an incentive for patients to live a fuller and more fulfilling life. This work presents a pipeline for the development of survivability prediction models and a system that provides survivability predictions for years one to five after the treatment of patients with colon or rectal cancer. The functionalities of the system are made available through a tool that balances the number of necessary inputs and prediction performance. It is mobile-friendly and facilitates the access of health care professionals to an instrument capable of enriching their practice and improving outcomes. The performance of survivability models was compared with other existing works in the literature and found to be an improvement over the current state of the art. The underlying system is capable of recalculating its prediction models upon the addition of new data, continuously evolving as time passes.
publishDate 2018
dc.date.none.fl_str_mv 2018-09-06
2018-09-06T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/60530
url http://hdl.handle.net/1822/60530
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1424-8220
10.3390/s18092983
30200676
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
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
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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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