Addressing the Curse of Missing Data in Clinical Contexts
Autor(a) principal: | |
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Data de Publicação: | 2023 |
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/10362/155244 |
Resumo: | Funding Information: This work was done under the project “CardioFollow.AI: An intelligent system to improve patients’ safety and remote surveillance in follow-up for cardiothoracic surgery”. Publisher Copyright: © 2023 The Author(s) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Addressing the Curse of Missing Data in Clinical ContextsA Novel Approach to Correlation-based ImputationClinical dataCorrelationMachine learningMissing dataMissing data imputationComputer Science(all)Funding Information: This work was done under the project “CardioFollow.AI: An intelligent system to improve patients’ safety and remote surveillance in follow-up for cardiothoracic surgery”. Publisher Copyright: © 2023 The Author(s)Clinical data are essential in the medical domain. However, their heterogeneous nature leads to many data quality problems, notably missing values, which undermine the performance of Machine Learning-based clinical systems. Hence, there has been a growing interest in strategies that address this challenge in order to build trustworthy systems to improve the quality of care and benefit clinical decision-making. In particular, missing value imputation is a common approach. This paper proposes three novel imputation techniques that leverage correlation in an innovative manner by exploring the relationship between values and missingness patterns. Experiments were carried out on three publicly available datasets, under three missingness mechanisms with different missing rates, and on two real-world medical datasets. The imputation precision and the classification performance of the proposed techniques were evaluated in a comprehensive comparative study, which included diverse existing methods. The developed techniques outperformed state-of-the-art methods on several assessments while overcoming current flaws shared by correlation-based imputation strategies in real-world medical problems.DF – Departamento de FísicaLIBPhys-UNLComprehensive Health Research Centre (CHRC) - pólo NMSNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNCurioso, IsabelSantos, RicardoRibeiro, BrunoCarreiro, AndréCoelho, PedroFragata, JoséGamboa, Hugo2023-07-13T22:17:45Z2023-062023-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12application/pdfhttp://hdl.handle.net/10362/155244eng1319-1578PURE: 66001651https://doi.org/10.1016/j.jksuci.2023.101562info: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:RCAAP2024-03-11T05:37:46Zoai:run.unl.pt:10362/155244Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:59.486650Repositório 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 |
Addressing the Curse of Missing Data in Clinical Contexts A Novel Approach to Correlation-based Imputation |
title |
Addressing the Curse of Missing Data in Clinical Contexts |
spellingShingle |
Addressing the Curse of Missing Data in Clinical Contexts Curioso, Isabel Clinical data Correlation Machine learning Missing data Missing data imputation Computer Science(all) |
title_short |
Addressing the Curse of Missing Data in Clinical Contexts |
title_full |
Addressing the Curse of Missing Data in Clinical Contexts |
title_fullStr |
Addressing the Curse of Missing Data in Clinical Contexts |
title_full_unstemmed |
Addressing the Curse of Missing Data in Clinical Contexts |
title_sort |
Addressing the Curse of Missing Data in Clinical Contexts |
author |
Curioso, Isabel |
author_facet |
Curioso, Isabel Santos, Ricardo Ribeiro, Bruno Carreiro, André Coelho, Pedro Fragata, José Gamboa, Hugo |
author_role |
author |
author2 |
Santos, Ricardo Ribeiro, Bruno Carreiro, André Coelho, Pedro Fragata, José Gamboa, Hugo |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
DF – Departamento de Física LIBPhys-UNL Comprehensive Health Research Centre (CHRC) - pólo NMS NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) RUN |
dc.contributor.author.fl_str_mv |
Curioso, Isabel Santos, Ricardo Ribeiro, Bruno Carreiro, André Coelho, Pedro Fragata, José Gamboa, Hugo |
dc.subject.por.fl_str_mv |
Clinical data Correlation Machine learning Missing data Missing data imputation Computer Science(all) |
topic |
Clinical data Correlation Machine learning Missing data Missing data imputation Computer Science(all) |
description |
Funding Information: This work was done under the project “CardioFollow.AI: An intelligent system to improve patients’ safety and remote surveillance in follow-up for cardiothoracic surgery”. Publisher Copyright: © 2023 The Author(s) |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-13T22:17:45Z 2023-06 2023-06-01T00:00:00Z |
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/10362/155244 |
url |
http://hdl.handle.net/10362/155244 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1319-1578 PURE: 66001651 https://doi.org/10.1016/j.jksuci.2023.101562 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
12 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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
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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1799138145917206528 |