Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions Nan, Yang Herrera Triguero, Francisco Information fusion Data harmonisation Data standardisation Domain adaptation Reproducibility This study was supported in part by the European Research Council Innovative Medicines Initiative (DRAGON#, H2020-JTI-IMI2 101005122), the AI for Health Imaging Award (CHAIMELEON##, H2020-SC1-FA-DTS-2019-1 952172), the UK Research and Innovation Future Leaders Fellowship (MR/V023799/1), the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), the SABRE project supported by Boehringer Ingelheim Ltd, the European Union's Horizon 2020 research and innovation programme (ICOVID, 101016131), the Euskampus Foundation (COVID19 Resilience, Ref. COnfVID19), and the Basque Government (consolidated research group MATHMODE, Ref. IT1294-19, and 3KIA project from the ELKARTEK funding program, Ref. KK-2020/00049). 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. 2022-05-06T11:59:59Z 2022-05-06T11:59:59Z 2022-01-24 info:eu-repo/semantics/article Yang Nan... [et al.]. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions, Information Fusion, Volume 82, 2022, Pages 99-122, ISSN 1566-2535, [https://doi.org/10.1016/j.inffus.2022.01.001] http://hdl.handle.net/10481/74733 10.1016/j.inffus.2022.01.001 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España Elsevier