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Review
. 2019 Jan;25(1):24-29.
doi: 10.1038/s41591-018-0316-z. Epub 2019 Jan 7.

A guide to deep learning in healthcare

Andre Esteva #  1 , Alexandre Robicquet #  2 , Bharath Ramsundar  2 , Volodymyr Kuleshov  2 , Mark DePristo  3 , Katherine Chou  3 , Claire Cui  3 , Greg Corrado  3 , Sebastian Thrun  2 , Jeff Dean  3
Affiliations
Review

A guide to deep learning in healthcare

Andre Esteva et al. Nat Med. 2019 Jan.

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.

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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).

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