Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions: A state-of-the-art systematic review, meta-analysis and future research directions

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dc.contributor.authorNan, Yang
dc.contributor.authorSer, Javier Del
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.authorPastor, Ana
dc.contributor.authorAlberich-Bayarri, Angel
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, Francisco
dc.contributor.authorYang, Guang
dc.contributor.institutionIA
dc.date.issued2022-06
dc.descriptionPublisher Copyright: © 2022 The Author(s)
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.en
dc.description.statusPeer reviewed
dc.format.extent24
dc.format.extent6870219
dc.identifier.citationNan , Y , Ser , J D , Walsh , S , Schönlieb , C , Roberts , M , Selby , I , Howard , K , Owen , J , Neville , J , Guiot , J , Ernst , B , Pastor , A , Alberich-Bayarri , A , Menzel , M I , Walsh , S , Vos , W , Flerin , N , Charbonnier , J-P , van Rikxoort , E , Chatterjee , A , Woodruff , H , Lambin , P , Cerdá-Alberich , L , Martí-Bonmatí , L , Herrera , F & Yang , G 2022 , ' Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions : A state-of-the-art systematic review, meta-analysis and future research directions ' , Information Fusion , vol. 82 , pp. 99-122 . https://doi.org/10.1016/j.inffus.2022.01.001
dc.identifier.doi10.1016/j.inffus.2022.01.001
dc.identifier.issn1566-2535
dc.identifier.otherresearchoutputwizard: 11556/1267
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85123598870&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofInformation Fusion
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsInformation fusion
dc.subject.keywordsData harmonisation
dc.subject.keywordsData standardisation
dc.subject.keywordsDomain adaptation
dc.subject.keywordsReproducibility
dc.subject.keywordsInformation fusion
dc.subject.keywordsData harmonisation
dc.subject.keywordsData standardisation
dc.subject.keywordsDomain adaptation
dc.subject.keywordsReproducibility
dc.subject.keywordsSoftware
dc.subject.keywordsSignal Processing
dc.subject.keywordsInformation Systems
dc.subject.keywordsHardware and Architecture
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/101005122/EU/The RapiD and SecuRe AI enhAnced DiaGnosis, Precision Medicine and Patient EmpOwerment Centered Decision Support System for Coronavirus PaNdemics/DRAGON
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the lab to market transition of AI tools for cancer Management/CHAIMELEON
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/101016131/EU/AI-based chest CT analysis enabling rapid COVID diagnosis and prognosis/icovid
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/101005122/EU/The RapiD and SecuRe AI enhAnced DiaGnosis, Precision Medicine and Patient EmpOwerment Centered Decision Support System for Coronavirus PaNdemics/DRAGON
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the lab to market transition of AI tools for cancer Management/CHAIMELEON
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/101016131/EU/AI-based chest CT analysis enabling rapid COVID diagnosis and prognosis/icovid
dc.subject.keywordsFunding Info
dc.subject.keywordsThis study was supported in part by the European Research Council_x000D_ Innovative Medicines Initiative (DRAGON#, H2020-JTI-IMI2_x000D_ 101005122), the AI for Health Imaging Award (CHAIMELEON##,_x000D_ H2020-SC1-FA-DTS-2019–1 952172), the UK Research and Innovation_x000D_ Future Leaders Fellowship (MR/V023799/1), the British Hear Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), the_x000D_ SABRE project supported by Boehringer Ingelheim Ltd, the European_x000D_ Union’s Horizon 2020 research and innovation programme (ICOVID,_x000D_ 101016131), the Euskampus Foundation (COVID19 Resilience,_x000D_ Ref. COnfVID19), and the Basque Government (consolidated research_x000D_ group MATHMODE, Ref. IT1294–19, and 3KIA project from the_x000D_ ELKARTEK funding program, Ref. KK-2020/00049)
dc.subject.keywordsThis study was supported in part by the European Research Council_x000D_ Innovative Medicines Initiative (DRAGON#, H2020-JTI-IMI2_x000D_ 101005122), the AI for Health Imaging Award (CHAIMELEON##,_x000D_ H2020-SC1-FA-DTS-2019–1 952172), the UK Research and Innovation_x000D_ Future Leaders Fellowship (MR/V023799/1), the British Hear Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), the_x000D_ SABRE project supported by Boehringer Ingelheim Ltd, the European_x000D_ Union’s Horizon 2020 research and innovation programme (ICOVID,_x000D_ 101016131), the Euskampus Foundation (COVID19 Resilience,_x000D_ Ref. COnfVID19), and the Basque Government (consolidated research_x000D_ group MATHMODE, Ref. IT1294–19, and 3KIA project from the_x000D_ ELKARTEK funding program, Ref. KK-2020/00049)
dc.titleData harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions: A state-of-the-art systematic review, meta-analysis and future research directionsen
dc.typejournal article
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