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

Published version
Peer-reviewed

Type

Article

Change log

Authors

Nan, Yang 
Ser, Javier Del 
Walsh, Simon 
Schönlieb, Carola 

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.

Description

Keywords

Information fusion, data harmonisation, data standardisation, domain adaptation, reproducibility

Journal Title

Inf Fusion

Conference Name

Journal ISSN

1566-2535
1872-6305

Volume Title

82

Publisher

Elsevier BV
Sponsorship
EPSRC (EP/T017961/1)