Predicting postoperative complications in cancer patients
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/128458 |
Resumo: | Postoperative complications can impose a significant burden, increasing morbidity, mortal-ity, and the in-hospital length of stay. Today, the number of studies available on the prognostication of postsurgical complications in cancer patients is growing and has already created a considerable set of dispersed contributions. This work provides a comprehensive survey on postoperative risk analysis, integrating principles from classic risk scores and machine-learning approaches within a coherent frame. A qualitative comparison is offered, taking into consideration the available cohort data and the targeted postsurgical outcomes of morbidity (such as the occurrence, nature or severity of postsurgical complications and hospitalization needs) and mortality. This work further establishes a taxonomy to assess the adequacy of cohort studies and guide the development and assessment of new learning approaches for the study and prediction of postoperative complications. |
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Predicting postoperative complications in cancer patientsA survey bridging classical and machine learning contributions to postsurgical risk analysisCancerClinical prognosisMachine learningPostoperative outcomesPostsurgical riskSurveyOncologyCancer ResearchSDG 3 - Good Health and Well-beingPostoperative complications can impose a significant burden, increasing morbidity, mortal-ity, and the in-hospital length of stay. Today, the number of studies available on the prognostication of postsurgical complications in cancer patients is growing and has already created a considerable set of dispersed contributions. This work provides a comprehensive survey on postoperative risk analysis, integrating principles from classic risk scores and machine-learning approaches within a coherent frame. A qualitative comparison is offered, taking into consideration the available cohort data and the targeted postsurgical outcomes of morbidity (such as the occurrence, nature or severity of postsurgical complications and hospitalization needs) and mortality. This work further establishes a taxonomy to assess the adequacy of cohort studies and guide the development and assessment of new learning approaches for the study and prediction of postoperative complications.LAQV@REQUIMTERUNGonçalves, Daniel M.Henriques, RuiCosta, Rafael S.2021-11-29T23:39:39Z2021-07-012021-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/128458eng2072-6694PURE: 34773505https://doi.org/10.3390/cancers13133217info: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:RCAAP2024-03-11T05:08:01Zoai:run.unl.pt:10362/128458Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:18.477479Repositório 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 |
Predicting postoperative complications in cancer patients A survey bridging classical and machine learning contributions to postsurgical risk analysis |
title |
Predicting postoperative complications in cancer patients |
spellingShingle |
Predicting postoperative complications in cancer patients Gonçalves, Daniel M. Cancer Clinical prognosis Machine learning Postoperative outcomes Postsurgical risk Survey Oncology Cancer Research SDG 3 - Good Health and Well-being |
title_short |
Predicting postoperative complications in cancer patients |
title_full |
Predicting postoperative complications in cancer patients |
title_fullStr |
Predicting postoperative complications in cancer patients |
title_full_unstemmed |
Predicting postoperative complications in cancer patients |
title_sort |
Predicting postoperative complications in cancer patients |
author |
Gonçalves, Daniel M. |
author_facet |
Gonçalves, Daniel M. Henriques, Rui Costa, Rafael S. |
author_role |
author |
author2 |
Henriques, Rui Costa, Rafael S. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
LAQV@REQUIMTE RUN |
dc.contributor.author.fl_str_mv |
Gonçalves, Daniel M. Henriques, Rui Costa, Rafael S. |
dc.subject.por.fl_str_mv |
Cancer Clinical prognosis Machine learning Postoperative outcomes Postsurgical risk Survey Oncology Cancer Research SDG 3 - Good Health and Well-being |
topic |
Cancer Clinical prognosis Machine learning Postoperative outcomes Postsurgical risk Survey Oncology Cancer Research SDG 3 - Good Health and Well-being |
description |
Postoperative complications can impose a significant burden, increasing morbidity, mortal-ity, and the in-hospital length of stay. Today, the number of studies available on the prognostication of postsurgical complications in cancer patients is growing and has already created a considerable set of dispersed contributions. This work provides a comprehensive survey on postoperative risk analysis, integrating principles from classic risk scores and machine-learning approaches within a coherent frame. A qualitative comparison is offered, taking into consideration the available cohort data and the targeted postsurgical outcomes of morbidity (such as the occurrence, nature or severity of postsurgical complications and hospitalization needs) and mortality. This work further establishes a taxonomy to assess the adequacy of cohort studies and guide the development and assessment of new learning approaches for the study and prediction of postoperative complications. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-29T23:39:39Z 2021-07-01 2021-07-01T00:00:00Z |
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/10362/128458 |
url |
http://hdl.handle.net/10362/128458 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2072-6694 PURE: 34773505 https://doi.org/10.3390/cancers13133217 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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