Beyond supervised learning for pervasive healthcare
File(s)
Accepted version
Author(s)
Type
Journal Article
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.
Date Issued
2024
Date Acceptance
2023-07-14
Citation
IEEE Reviews in Biomedical Engineering, 2024, 17, pp.42-62
ISSN
1937-3333
Publisher
Institute of Electrical and Electronics Engineers
Start Page
42
End Page
62
Journal / Book Title
IEEE Reviews in Biomedical Engineering
Volume
17
Copyright Statement
Copyright © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/10189101
Publication Status
Published
Date Publish Online
2023-07-20