VOOZH about

URL: https://pubmed.ncbi.nlm.nih.gov/30617335/

⇱ A guide to deep learning in healthcare - PubMed


Clipboard, Search History, and several other advanced features are temporarily unavailable.
Skip to main page content
πŸ‘ Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

πŸ‘ Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Abstract

Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.

PubMed Disclaimer

References

    1. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
    1. Russakovsky, O.et al. Imagenet large scale visual recognition challenge. Int. J. Compute. Vis. 115, 211–252 (2015).
    1. Hirschberg, J. & Manning, C. D. Advances in natural language processing. Science 349, 261–266 2015). - DOI
    1. Geoffrey Hinton, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29, 82–97 (2012).
    1. Stanford Health. Harnessing the power of data in health. Stanford Medicine 2017 Health Trends Report (2017).
Cite

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.