监督学习方法及其在医学研究中的应用

Yu Ming, Lily Yuan
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引用次数: 2

摘要

机器学习(ML)和人工智能(AI)方法正在改变许多商业和学术领域,包括特征提取、自动驾驶、计算语言学和语音识别。这些新技术现在对放射照相、法医和许多其他领域产生了重大影响,在这些领域,自动化系统的可访问性可以提高基本工作性能的精度和可重复性。在这篇系统综述中,我们首先简要概述了目前正在开发的不同方法,特别强调了生物医学研究中使用的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Supervised Learning Methods and Applications in Medical Research
Machine Learning (ML) and Artificial Intelligence (AI) methods are transforming many commercial and academic areas, including feature extraction, autonomous driving, computational linguistics, and voice recognition. These new technologies are now having a significant effect in radiography, forensics, and many other areas where the accessibility of automated systems may improve the precision and repeatability of essential job performance. In this systematic review, we begin by providing a short overview of the different methods that are currently being developed, with a particular emphasis on those utilized in biomedical studies.
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