Beyond supervised learning for pervasive healthcare

Gu, X., Deligianni, F. , Han, J., Liu, X., Chen, W., Yang, G.-Z. and Lo, B. (2023) Beyond supervised learning for pervasive healthcare. IEEE Reviews in Biomedical Engineering, (doi: 10.1109/RBME.2023.3296938) (Early Online Publication)

Abstract

The integration of machine/deep learning and sensing technologies is transforming healthcare and medical practice. However, inherent limitations in healthcare data, namely scarcity , quality , and heterogeneity , hinder the effectiveness of supervised learning techniques which are mainly based on pure statistical fitting between data and labels. In this paper, we first identify the challenges present in machine learning for pervasive healthcare and we then review the current trends beyond fully supervised learning that are developed to address these three issues. Rooted in the inherent drawbacks of empirical risk minimization that underpins pure fully supervised learning, this survey summarizes seven key lines of learning strategies, to promote the generalization performance for real-world deployment. In addition, we point out several directions that are emerging and promising in this area, to develop data-efficient, scalable, and trustworthy computational models, and to leverage multi-modality and multi-source sensing informatics, for pervasive healthcare.

• Item Type: Articles
• Additional Information: The authors would like to acknowledge Engineering and Physical Sciences Research Council (EP/W01212X/1), the Royal Society Research Grant (RGS/R2/212199), Shanghai Municipal Science and Technology International R&D Collaboration Project (Grant No.20510710500), and Shanghai Sailing Program (22YF 1430800).
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Gu, Mr Xiao and Deligianni, Dr Fani
• Authors: Gu, X., Deligianni, F., Han, J., Liu, X., Chen, W., Yang, G.-Z., and Lo, B.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: IEEE Reviews in Biomedical Engineering
• Publisher: IEEE
• ISSN: 1937-3333
• ISSN (Online): 1941-1189
• Published Online: 20 July 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Reviews in Biomedical Engineering 2023
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
315206		Privacy-Preserved Human Motion Analysis for Healthcare Applications	Fani Deligianni	Engineering and Physical Sciences Research Council (EPSRC)	EP/W01212X/1	Computing Science
315626		Privacy-preserved Human Motion Analysis	Fani Deligianni	The Royal Society (ROYSOC)	RGS\R2\212199	Computing Science