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Autism Spectrum Disorder01:19

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
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Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism
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在使用机器学习对语音成绩单的儿童中查自闭症谱系障碍.

Rida Assaf1, Zein Shehabeddine2, Vikram Ramesh3

  • 1Department of Computer Science, American University of Beirut, Beirut, Lebanon. ra278@aub.edu.lb.

Scientific reports
|October 1, 2025
PubMed
概括

这项研究表明,使用儿童语音转录的机器学习模型可以以超过86%的准确度检测自闭症谱系障碍 (ASD). 这种保护隐私的方法为传统诊断工具提供了更快,更道德的替代方案.

关键词:
人工智能的人工智能是人工智能.自闭症谱系障碍 (ASD) 是一种计算语言学计算语言学功能重要性 功能重要性机器学习 机器学习隐私 隐私 隐私 隐私 隐私 隐私

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科学领域:

  • 计算语言学 计算语言学
  • 发展心理学 发展心理学
  • 机器学习 机器学习

背景情况:

  • 早期发现自闭症谱系障碍 (ASD) 对有效干预和改善发展结果至关重要.
  • 传统的ASD诊断方法往往耗时,资源密集,并引发隐私问题,特别是对于未成年人.
  • 需要采用非侵入性和道德的方法来检测ASD.

研究的目的:

  • 评估保护隐私的机器学习模型的可行性,以使用儿童语音成绩单来检测ASD.
  • 评估基于文本的语言特征对识别自闭症的有效性.
  • 探索能够尽量减少与敏感生物识别数据相关的隐私风险的方法.

主要方法:

  • 利用机器学习模型,对儿童的结构化基于文本的语音数据进行训练.
  • 专注于语言特征,如平均发音长度 (MLU) 和平均转折长度比 (MLT比).
  • 在TalkBank存储库中的两个数据集上进行了实验,通过避免原始音频/视频数据来优先考虑隐私.

主要成果:

  • 机器学习模型在两个数据集上都实现了超过86%的预测准确度.
  • 一小部分的语言特征被证明足以实现高性能,减少数据收集需求.
  • 基于文本的方法通过排除可识别的生物识别数据来降低隐私风险.

结论:

  • 使用语音转录的保护隐私的机器学习模型显示了早期ASD检测的重大前景.
  • 计算语言学为未来的ASD诊断工具在临床和教育环境中提供了一个非侵入性和道德基础.
  • 这种方法通过专注于结构化的语言数据而不是敏感的生物识别信息来增强隐私.