TY - JOUR A1 - Nan, Yang A1 - Del Ser, Javier 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 Irene A1 - Walsh, Sean A1 - Vos, Wim A1 - Flerin, Nina A1 - Charbonnier, Jean-Paul A1 - Rikxoort, Eva van A1 - Chatterjee, Avishek A1 - Woodruff, Henry A1 - Lambin, Philippe A1 - Cerdá-Alberich, Leonor A1 - Martí-Bonmatí, Luis A1 - Herrera, Francisco A1 - Yang, Guang T1 - Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions JF - Information Fusion N2 - 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 UR - https://doi.org/10.1016/j.inffus.2022.01.001 KW - Information fusion KW - data harmonisation KW - data standardisation KW - domain adaptation KW - reproducibility Y1 - 2022 UR - https://doi.org/10.1016/j.inffus.2022.01.001 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13845 SN - 1566-2535 N1 - Der Nachweis einer Preprint-Version dieser Veröffentlichung ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4643 VL - 2022 IS - 82 SP - 99 EP - 122 PB - Elsevier CY - Amsterdam ER - TY - INPR A1 - Nan, Yang A1 - Del Ser, Javier 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 Irene A1 - Walsh, Sean A1 - Vos, Wim A1 - Flerin, Nina A1 - Charbonnier, Jean-Paul A1 - Rikxoort, Eva van A1 - Chatterjee, Avishek A1 - Woodruff, Henry A1 - Lambin, Philippe A1 - Cerdá-Alberich, Leonor A1 - Martí-Bonmatí, Luis A1 - Herrera, Francisco A1 - Yang, Guang T1 - Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions N2 - 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. UR - https://doi.org/10.48550/arXiv.2201.06505 Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2201.06505 N1 - Die veröffentlichte Version dieses Preprints ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/1384 PB - arXiv CY - Ithaca ER -