语音语言病理学家的机器学习介绍:概念,术语和新兴应用
Claire Cordella1, Manuel J Marte1, Hantian Liu1,2
1Center for Brain Recovery, Boston University, MA.
Perspectives of the ASHA special interest groups
|August 11, 2025
概括
这篇文章介绍了机器学习 (ML) 与语音语言病理学,涵盖了概念,应用和挑战. 它旨在指导临床医生和研究人员负责任地使用ML以获得更好的患者结果.
科学领域:
- 语音语言病理学 语言病理学
- 计算语言学计算语言学
- 人工智能的人工智能是人工智能.
背景情况:
- 机器学习 (ML) 提供了先进的分析能力.
- 语音语言病理学的ML的整合是一个新兴的领域.
- 了解ML对于未来的研究和临床实践至关重要.
研究的目的:
- 为临床医生和研究人员介绍机器学习 (ML) 在语音语言病理学的方法.
- 提供关键的ML概念,术语和模型的概述.
- 讨论 ML 在现场实施的挑战和局限性.
主要方法:
- 介绍基本的ML概念 (如分类,回归).
- 解释各种ML模型 (例如随机森林,支持矢量机器,神经网络).
- 对六项应用ML在aphasiology中的示例研究的审查.
主要成果:
- 通过案例研究,展示ML在语音语言病理学中的应用.
- 讨论ML模型开发和评估的关键考虑.
- 识别限制,包括可靠性,有效性,偏见和可解释性.
结论:
- 机器学习对于推进语音语言病理学的潜力很大.
- 解决可靠性,有效性和可解释性方面的挑战是临床采用的关键.
- 负责任地实施ML对于有意义的临床影响至关重要.
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