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dc.contributor.authorNan, Yang
dc.contributor.authorDel Ser Lorente, Javier ORCID
dc.contributor.authorWalsh, Simon
dc.contributor.authorSchönlieb, Carola
dc.contributor.authorRoberts, Michael
dc.contributor.authorSelby, Ian
dc.contributor.authorHoward, Kit
dc.contributor.authorOwen, John
dc.contributor.authorNeville, Jon
dc.contributor.authorGuiot, Julien
dc.contributor.authorErnst, Benoit
dc.contributor.authorJiménez Pastor, Ana
dc.contributor.authorAlberich Bayarri, Ángel
dc.contributor.authorMenzel, Marion I.
dc.contributor.authorWalsh, Sean
dc.contributor.authorVos, Wim
dc.contributor.authorFlerin, Nina
dc.contributor.authorCharbonnier, Jean Paul
dc.contributor.authorvan Rikxoort, Eva
dc.contributor.authorChatterjee, Avishek
dc.contributor.authorWoodruff, Henry
dc.contributor.authorLambin, Philippe
dc.contributor.authorCerdá Alberich, Leonor
dc.contributor.authorMartí Bonmatí, Luis
dc.contributor.authorHerrera Triguero, Francisco
dc.contributor.authorYang, Guang
dc.date.accessioned2022-09-02T10:38:18Z
dc.date.available2022-09-02T10:38:18Z
dc.date.issued2022-06
dc.identifier.citationInformation Fusion 82 : 99-122 (2022)es_ES
dc.identifier.issn1566-2535
dc.identifier.issn1872-6305
dc.identifier.urihttp://hdl.handle.net/10810/57424
dc.description.abstractRemoving the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.es_ES
dc.description.sponsorshipThis study was supported in part by the European Research Council Innovative Medicines Initiative (DRAGON#, H2020-JTI-IMI2 101005122), the AI for Health Imaging Award (CHAIMELEON##, H2020-SC1-FA-DTS-2019-1 952172), the UK Research and Innovation Future Leaders Fellowship (MR/V023799/1), the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), the SABRE project supported by Boehringer Ingelheim Ltd, the European Union's Horizon 2020 research and innovation programme (ICOVID, 101016131), the Euskampus Foundation (COVID19 Resilience, Ref. COnfVID19), and the Basque Government (consolidated research group MATHMODE, Ref. IT1294-19, and 3KIA project from the ELKARTEK funding program, Ref. KK-2020/00049).es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/952172es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/101016131es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/101005122es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectinformation fusiones_ES
dc.subjectdata harmonisationes_ES
dc.subjectdata standardisationes_ES
dc.subjectdomain adaptationes_ES
dc.subjectreproducibilityes_ES
dc.titleData harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directionses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S156625352200015X?via%3Dihubes_ES
dc.identifier.doi10.1016/j.inffus.2022.01.001
dc.contributor.funderEuropean Commission
dc.departamentoesIngeniería de comunicacioneses_ES
dc.departamentoeuKomunikazioen ingeniaritzaes_ES


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© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Except where otherwise noted, this item's license is described as © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)