Deep Learning for Health Informatics
Deep Learning for Health Informatics
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DOI：
10.1109/jbhi.2016.2636665
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发表时间：
2017-01-01
影响因子：
7.7
通讯作者：
Yang, Guang-Zhong
中科院分区：
文献类型：
--
作者：
Ravi, Daniele;Wong, Charence;Yang, Guang-Zhong
随着多模态数据的大量涌入，数据分析在健康信息学中的作用在过去十年中迅速增长。这也促使人们越来越关注基于健康信息学中机器学习的分析数据驱动模型的生成。深度学习是一种以人工神经网络为基础的技术，近年来正成为机器学习的强大工具，有望重塑人工智能的未来。计算能力、快速数据存储和并行化的快速提高也有助于该技术的快速发展，此外，该技术还具有预测能力和从输入数据中自动生成优化的高级特征和语义解释的能力。本文对在健康信息学中使用深度学习的研究进行了全面的最新回顾，对该技术的相对优点和潜在缺陷以及未来前景进行了批判性分析。本文主要关注深度学习在翻译生物信息学、医学成像、普适传感、医学信息学和公共卫生领域的关键应用。
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.