Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions
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Date
2022-06Author
Nan, Yang
Walsh, Simon
Schönlieb, Carola
Roberts, Michael
Selby, Ian
Howard, Kit
Owen, John
Neville, Jon
Guiot, Julien
Ernst, Benoit
Jiménez Pastor, Ana
Alberich Bayarri, Ángel
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 Triguero, Francisco
Yang, Guang
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Information Fusion 82 : 99-122 (2022)
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.