Cited by 128Machine Learning in HealthcareArtificial Intelligence in Healthcare and EducationComputational Drug Discovery Methods

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

Zhang, Sushen, Bamakan, Seyed Mojtaba Hosseini, Qu, Qiang, Li, Sha·2019·IEEE Reviews in Biomedical Engineering

DOI 10.1109/rbme.2018.2864254

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Abstract

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.

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Authors: Zhang, S., Bamakan, S. M. H., Qu, Q., & Li, S.
Affiliations: Chinese Academy of Sciences (CN)
ORCID: Zhang, Sushen
Bamakan, Seyed Mojtaba Hosseini
Qu, Qiang
Li, Sha
Venue: IEEE Reviews in Biomedical Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019
Volume: 12
Pages: 194-208
ISSN: 1937-3333 (ISSN-L)
1941-1189
DOI: 10.1109/rbme.2018.2864254
OpenAlex: W2887975519
Cited by: 125
References: 146
Citations by year: 2026: 7 · 2025: 21 · 2024: 30 · 2023: 20 · 2022: 19 · 2021: 17 · 2020: 9 · 2019: 2
FWCI: 0.81
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Reviews in Biomedical Engineering (publishedVersion)
PubMed (publishedVersion)
Funders: CAS Pioneer Hundred Talents Program, China (Y84402, 2017)
China Postdoctoral Science Foundation (2018M633186, 2018)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords: personalized medicine, deep learning, drug development, genetic data, machine learning in medicine
MeSH terms: Drug Development, Deep Learning, Humans, Research, Precision Medicine

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

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