Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations
Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations
复制标题
针对医疗保健挑战的深度学习干预：一些生物医学领域的考虑因素
DOI：
10.2196/11966
复制
发表时间：
2019-08-02
影响因子：
5
通讯作者：
Wang, Lei
中科院分区：
文献类型：
--
作者：
Tobore, Igbe;Li, Jingzhen;Wang, Lei
在过去的十年中，深度学习（DL）用于生物医学和医疗保健问题的分析和诊断受到了前所未有的关注。这项技术已经取得了许多成就，用于挖掘有意义的特征，并完成迄今为止难以通过其他方法和人类专家解决的任务。目前，生物和医疗设备、治疗和应用程序能够以图像、声音、文本、图形和信号的形式生成大量数据，从而创建了大数据的概念。数字图书馆的创新是随着大数据的出现而出现的一种数据表达和分析的发展趋势。DL是一种机器学习算法，它具有更深（或更多）的隐藏层，类似的功能级联到网络中，并有能力从医疗大数据中获得意义。当前实现个性化医疗保健服务的转型驱动因素将有可能通过使用移动的医疗保健（mHealth）实现。DL可以为mHealth应用程序生成的大量数据提供分析。本文回顾了DL方法的基本原理，并通过从PubMed和电气与电子工程师协会数据库出版物中获取文献，实现DL的不同变体，对DL的趋势进行了概述。重点介绍了数字图书馆在医疗保健领域的应用，分为生物系统、电子健康档案、医学影像和生理信号四类。此外，我们还讨论了深度学习影响生物医学和健康领域的一些固有挑战，以及通过促进生理信号和现代互联网技术的应用来改善健康管理的前瞻性研究方向。
The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.