A review on multimodal machine learning in medical diagnostics

Author:

Yan Keyue1,Li Tengyue1,Marques João Alexandre Lobo2,Gao Juntao3,Fong Simon James14

Affiliation:

1. Department of Computer and Information Science, University of Macau, Macau SAR, China

2. Laboratory of Applied Neurosciences, University of Saint Joseph, Macau SAR, China

3. Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China

4. Institute of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, China

Abstract

<abstract><p>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.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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