A guide to deep learning in healthcare

被引:2217
作者
Esteva, Andre [1 ]
Robicquet, Alexandre [1 ]
Ramsundar, Bharath [1 ]
Kuleshov, Volodymyr [1 ]
DePristo, Mark [2 ]
Chou, Katherine [2 ]
Cui, Claire [2 ]
Corrado, Greg [2 ]
Thrun, Sebastian [1 ]
Dean, Jeff [2 ]
机构
[1] Stanford Univ, Stanford, CA 94305 USA
[2] Google Res, San Jose, CA USA
关键词
NEURAL-NETWORKS; PATHOGENICITY; PREDICTION; CANCER; DNA;
D O I
10.1038/s41591-018-0316-z
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:24 / 29
页数:6
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