| Literature DB >> 30617335 |
Andre Esteva1, Alexandre Robicquet2, Bharath Ramsundar2, Volodymyr Kuleshov2, Mark DePristo3, Katherine Chou3, Claire Cui3, Greg Corrado3, Sebastian Thrun2, Jeff Dean3.
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.Entities:
Mesh:
Year: 2019 PMID: 30617335 DOI: 10.1038/s41591-018-0316-z
Source DB: PubMed Journal: Nat Med ISSN: 1078-8956 Impact factor: 53.440