On the predictability of postoperative complications for cancer patients

Detalhes bibliográficos
Autor(a) principal: Gonçalves, Daniel
Data de Publicação: 2021
Outros Autores: Henriques, Rui, Santos, Lúcio Lara, Costa, Rafael S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/128322
Resumo: DSAIPA/DS/0044/2018
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spelling On the predictability of postoperative complications for cancer patientsa Portuguese cohort studyCancerClinical decision support systemData modelingMachine learningPostoperative complicationsRisk predictionHealth PolicyHealth InformaticsSDG 3 - Good Health and Well-beingDSAIPA/DS/0044/2018Postoperative complications are still hard to predict despite the efforts towards the creation of clinical risk scores. The published scores contribute for the creation of specialized tools, but with limited predictive performance and reusability for implementation in the oncological context. This work aims to predict postoperative complications risk for cancer patients, offering two major contributions. First, to develop and evaluate a machine learning-based risk score, specific for the Portuguese population using a retrospective cohort of 847 cancer patients undergoing surgery between 2016 and 2018, for 4 outcomes of interest: (1) existence of postoperative complications, (2) severity level of complications, (3) number of days in the Intermediate Care Unit (ICU), and (4) postoperative mortality within 1 year. An additional cohort of 137 cancer patients from the same center was used for validation. Second, to improve the interpretability of the predictive models. In order to achieve these objectives, we propose an approach for the learning of risk predictors, offering new perspectives and insights into the clinical decision process. For postoperative complications the Receiver Operating Characteristic Curve (AUC) was 0.69, for complications’ severity AUC was 0.65, for the days in the ICU the mean absolute error was 1.07 days, and for 1-year postoperative mortality the AUC was 0.74, calculated on the development cohort. In this study, predictive models which could help to guide physicians at organizational and clinical decision making were developed. Additionally, a web-based decision support tool is further provided to this end.LAQV@REQUIMTERUNGonçalves, DanielHenriques, RuiSantos, Lúcio LaraCosta, Rafael S.2021-11-26T23:44:49Z2021-122021-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/128322eng1472-6947PURE: 34773425https://doi.org/10.1186/s12911-021-01562-2info: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:RCAAP2024-03-11T05:07:58Zoai:run.unl.pt:10362/128322Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:17.430405Repositório 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 On the predictability of postoperative complications for cancer patients
a Portuguese cohort study
title On the predictability of postoperative complications for cancer patients
spellingShingle On the predictability of postoperative complications for cancer patients
Gonçalves, Daniel
Cancer
Clinical decision support system
Data modeling
Machine learning
Postoperative complications
Risk prediction
Health Policy
Health Informatics
SDG 3 - Good Health and Well-being
title_short On the predictability of postoperative complications for cancer patients
title_full On the predictability of postoperative complications for cancer patients
title_fullStr On the predictability of postoperative complications for cancer patients
title_full_unstemmed On the predictability of postoperative complications for cancer patients
title_sort On the predictability of postoperative complications for cancer patients
author Gonçalves, Daniel
author_facet Gonçalves, Daniel
Henriques, Rui
Santos, Lúcio Lara
Costa, Rafael S.
author_role author
author2 Henriques, Rui
Santos, Lúcio Lara
Costa, Rafael S.
author2_role author
author
author
dc.contributor.none.fl_str_mv LAQV@REQUIMTE
RUN
dc.contributor.author.fl_str_mv Gonçalves, Daniel
Henriques, Rui
Santos, Lúcio Lara
Costa, Rafael S.
dc.subject.por.fl_str_mv Cancer
Clinical decision support system
Data modeling
Machine learning
Postoperative complications
Risk prediction
Health Policy
Health Informatics
SDG 3 - Good Health and Well-being
topic Cancer
Clinical decision support system
Data modeling
Machine learning
Postoperative complications
Risk prediction
Health Policy
Health Informatics
SDG 3 - Good Health and Well-being
description DSAIPA/DS/0044/2018
publishDate 2021
dc.date.none.fl_str_mv 2021-11-26T23:44:49Z
2021-12
2021-12-01T00:00:00Z
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/10362/128322
url http://hdl.handle.net/10362/128322
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1472-6947
PURE: 34773425
https://doi.org/10.1186/s12911-021-01562-2
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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