@misc{10481/74733,
year = {2022},
month = {1},
url = {http://hdl.handle.net/10481/74733},
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.},
organization = {European Research Council Innovative Medicines Initiative	H2020-JTI-IMI2 101005122},
organization = {AI for Health Imaging Award	H2020-SC1-FA-DTS-2019-1 952172},
organization = {UK Research & Innovation (UKRI)	MR/V023799/1},
organization = {British Heart Foundation	TG/18/5/34111	
PG/16/78/32402},
organization = {Boehringer Ingelheim},
organization = {European Commission	101016131},
organization = {Euskampus Foundation	COnfVID19},
organization = {Basque Government	IT1294-19},
organization = {Basque Government (3KIA project from the ELKARTEK funding program)	KK-2020/00049},
publisher = {Elsevier},
keywords = {Information fusion},
keywords = {Data harmonisation},
keywords = {Data standardisation},
keywords = {Domain adaptation},
keywords = {Reproducibility},
title = {Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions},
doi = {10.1016/j.inffus.2022.01.001},
author = {Nan, Yang and Herrera Triguero, Francisco},
}