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

Nan, Yang; Ser, Javier Del; Walsh, Simon; Schönlieb, Carola; Roberts, Michael; Selby, Ian; Howard, Kit; Owen, John; Neville, Jon; Guiot, Julien; Ernst, Benoit; Pastor, Ana; Alberich-Bayarri, Angel; Menzel, Marion I.; Walsh, Sean; Vos, Wim; Flerin, Nina; Charbonnier, Jean-Paul; van Rikxoort, Eva; Chatterjee, Avishek; Woodruff, Henry; Lambin, Philippe; Cerdá-Alberich, Leonor; Martí-Bonmatí, Luis; Herrera, Francisco; Yang, Guang

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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ISSN: 1566-2535

DOI: 10.1016/j.inffus.2022.01.001

Publication date

2022-06

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Abstract

Removing 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.

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Keywords

Information fusion , Data harmonisation , Data standardisation , Domain adaptation , Reproducibility , Information fusion , Data harmonisation , Data standardisation , Domain adaptation , Reproducibility , Software , Signal Processing , Information Systems , Hardware and Architecture , Project ID , info: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 , info:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the lab to market transition of AI tools for cancer Management/CHAIMELEON , info:eu-repo/grantAgreement/EC/H2020/101016131/EU/AI-based chest CT analysis enabling rapid COVID diagnosis and prognosis/icovid , info: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 , info:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the lab to market transition of AI tools for cancer Management/CHAIMELEON , info:eu-repo/grantAgreement/EC/H2020/101016131/EU/AI-based chest CT analysis enabling rapid COVID diagnosis and prognosis/icovid , Funding Info , This 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) , This 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)

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journal article

Citation

Nan , 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

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