深度学习工具在医疗保健大数据分析中的有效性研究

R. Priyadarshini, Rabindra Kumar Barik, C. Panigrahi, Harishchandra Dubey, B. K. Mishra
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引用次数: 4

摘要

本文描述了机器学习(ML)算法如何在分析数据和从中找到一些有意义的信息方面非常有用,这些信息可以用于各种其他应用程序。在过去的几年里,数据的规模和结构都出现了爆炸式的增长。在处理如此大量和非结构化的大数据时,传统的ML算法面临着一些困难。现代机器学习工具的设计和使用是为了处理各种复杂的数据。深度学习(Deep learning, DL)是现代机器学习工具之一,通常用于在并行平台上通过智能优化技术进行适当的训练,以进一步分析和解释数据以进行未来预测和分类,从而发现这些大型数据集之间的隐藏结构和内聚性。本文重点介绍过去几年在各个领域,特别是医疗保健应用领域中使用的深度学习工具和软件的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care
This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.
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