Addressing the Curse of Missing Data in Clinical Contexts

Detalhes bibliográficos
Autor(a) principal: Curioso, Isabel
Data de Publicação: 2023
Outros Autores: Santos, Ricardo, Ribeiro, Bruno, Carreiro, André, Coelho, Pedro, Fragata, José, Gamboa, Hugo
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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spelling 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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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