A review on multimodal machine learning in medical diagnostics

Resource type
Authors/contributors
Title
A review on multimodal machine learning in medical diagnostics
Abstract
Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research.
Publication
Mathematical Biosciences and Engineering
Volume
20
Issue
5
Pages
8708-8726
Date
2023
Journal Abbr
MBE
Language
en
ISSN
1551-0018
Accessed
3/21/23, 4:22 PM
Library Catalog
Rights
2023 The Author(s)
Extra
Cc_license_type: cc_by Number: mbe-20-05-382 Primary_atype: Mathematical Biosciences and Engineering Subject_term: Review Subject_term_id: Review
Citation
Yan, K., Li, T., Marques, J. A. L., Gao, J., Fong, S. J., Yan, K., Li, T., Marques, J. A. L., Gao, J., & Fong, S. J. (2023). A review on multimodal machine learning in medical diagnostics. Mathematical Biosciences and Engineering, 20(5), 8708–8726. https://doi.org/10.3934/mbe.2023382