RT Journal Article
T1 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
A1 Nan, Yang
A1 Ser, Javier Del
A1 Walsh, Simon
A1 Schönlieb, Carola
A1 Roberts, Michael
A1 Selby, Ian
A1 Howard, Kit
A1 Owen, John
A1 Neville, Jon
A1 Guiot, Julien
A1 Ernst, Benoit
A1 Pastor, Ana
A1 Alberich-Bayarri, Angel
A1 Menzel, Marion I.
A1 Walsh, Sean
A1 Vos, Wim
A1 Flerin, Nina
A1 Charbonnier, Jean-Paul
A1 van Rikxoort, Eva
A1 Chatterjee, Avishek
A1 Woodruff, Henry
A1 Lambin, Philippe
A1 Cerdá-Alberich, Leonor
A1 Martí-Bonmatí, Luis
A1 Herrera, Francisco
A1 Yang, Guang
AB 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.
SN 1566-2535
YR 2022
FD 2022-06
LA eng
NO 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
NO Publisher Copyright: © 2022 The Author(s)
DS TECNALIA Publications
RD 1 jul 2024