Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective

With the recent advancements in analyzing high-volume, complex, and unstructured data, modern learning methods are playing an increasingly critical role in the field of personalized medicine. Personalized medicine (i.e., providing tailored medical treatment to individual patients through the identif...

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Bibliographic Details
Published in:IEEE reviews in biomedical engineering 2019-01, Vol.12, p.194-208
Main Authors: Zhang, Sushen, Bamakan, Seyed Mojtaba Hosseini, Qu, Qiang, Li, Sha
Format: Article
Language:English
Subjects:
Complexity
Data processing
Deep learning
Diseases
disease characteristics
Domains
Drug development
Drugs
Feature recognition
Genetics
Heredity
Learning systems
Machine learning
Machine learning algorithms
Medical research
Medical treatment
Medicine
Neural networks
Nonlinear systems
personalized medicine
Precision medicine
Teaching methods
Unstructured data
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Summary:With the recent advancements in analyzing high-volume, complex, and unstructured data, modern learning methods are playing an increasingly critical role in the field of personalized medicine. Personalized medicine (i.e., providing tailored medical treatment to individual patients through the identification of common features, including their genetics, inheritance, and lifestyle) has attracted the attention of many researchers in recent years. This paper provides an overview of the research progress in the application of learning methods, with a focus on deep learning in personalized medicine. In particular, three domains of applications are reviewed: drug development, disease characteristic identification, and therapeutic effect prediction. The main objective of this review is to consider the applied methods in detail and to offer insights into their pros and cons. Although having demonstrated advantages in coping with data complexity and nonlinearity and in recognizing features and associating structural data, the studied learning methods are not a panacea to all medical problems. Hence, we discuss the existing research challenges and clarify future study directions.
ISSN:1937-3333
1941-1189
DOI:10.1109/RBME.2018.2864254