Cited by 3,076Machine Learning in HealthcareArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Deep learning for healthcare: review, opportunities and challenges

Open access
Miotto, Riccardo, Wang, Fei, Wang, Shuang, Jiang, Xiaoqian, Dudley, Joel T·2018·Briefings in Bioinformatics

Abstract

Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.

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  • Multimodal DL approaches face the same challenges that DL in the biomedical field face generally, including data volume, quality, interpretability and temporality.

    “Multimodal DL approaches face the same challenges that DL in the biomedical field face generally, including data volume, quality, interpretability and temporality as outlined by e.g. Miotto et al. [100].”

    From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [100]

  • Machine learning and deep learning models are attracting much attention and are proving to be very practical in the healthcare industry.

    “The intelligent models of machine learning and deep learning are attracting much attention and are proving to be very practical [130,131] in the healthcare industry.”

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Research works printed but not linked: 6

  1. Cho K, Courville A, Bengio Y. Describing multimedia content using attention-based encoder-decoder networks. arXiv 2015. http://arxiv.org/abs/1507.01053 · arXiv
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Authors
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T.
Affiliations
  • Icahn School of Medicine at Mount Sinai (US)
  • Cornell University (US)
  • University of California San Diego (US)
Corresponding author
Wang, Fei
Venue
Briefings in Bioinformatics
Published
2017-05-06
Volume
19
Issue
6
Pages
1236-1246
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
OpenAlex
W2610332124
Cited by
3,076
References
114
Citations by year
2026: 353 · 2025: 538 · 2024: 422 · 2023: 393 · 2022: 371 · 2021: 345 · 2020: 310 · 2019: 216 · 2018: 111 · 2017: 15
FWCI
26.29
Type
journal article
Language
en
Open access
Locations
  • Briefings in Bioinformatics (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation (R01GM118609, 1650723)
  • National Institutes of Health (U01TR002062, UL1TR000067)
  • National Human Genome Research Institute (R00HG008175, R01HG007078)
  • National Cancer Institute (U54-CA189201-02, U54CA189201)
  • National Institute of General Medical Sciences (R01GM118609, R01GM114612)
  • National Institute of Diabetes and Digestive and Kidney Diseases (R01-DK098242-03, R01DK098242)
  • National Institute of Biomedical Imaging and Bioengineering (U01EB023685)
  • U.S. National Library of Medicine (R00LM011392, R21LM012060)
  • Division of Information and Intelligent Systems
  • National Center for Advancing Translational Sciences
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
deep learning in healthcare, biomedical data, electronic health records, diagnostic imaging, omics data, sensor data, text mining, end-to-end learning, feature engineering, model interpretability, explainable AI, citizen science, unstructured data
MeSH terms
Deep Learning, Delivery of Health Care, Diagnostic Imaging, Humans, Telemedicine, Computational Biology, Genomics, Data Mining, Electronic Health Records

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