利用大型语言模型来诊断自闭症相关的语言障碍,并识别不同的特征
Chuanbo Hu1, Wenqi Li1, Mindi Ruan2
1Department of Computer Science, University at Albany, Albany, 12222, NY, USA.
Research square
|August 6, 2025
概括
大型语言模型 (LLM) 通过分析语言模式,提高灵敏度和预测价值来改善自闭症诊断. 这项技术有助于确定定制治疗计划的关键特征.
科学领域:
- 计算语言学计算语言学
- 发展心理学是发展心理学.
- 医疗保健中的人工智能
背景情况:
- 在自闭症谱系障碍 (ASD) 中诊断语言障碍是具有挑战性的,因为主观的传统方法.
- 现有的评估工具往往缺乏及时诊断所需的速度和精度.
研究的目的:
- 探索大语言模型 (LLM) 的使用,以提高自闭症诊断的灵敏度和精度.
- 识别和描述与ASD相关的语言障碍相关的关键语言特征.
主要方法:
- 利用LLM的自然语言理解能力来分析ASD患者的语言模式.
- 使用零射击学习配置来评估模型性能与基线方法相比.
主要成果:
- 基于LLM的方法显示,与基线模型相比,灵敏度和积极预测值增加了10%以上.
- 确定了十个关键的语言特征,包括声和代词反转,对于自闭症诊断至关重要.
结论:
- LLM提供了一个有希望的补充工具,可以提高诊断ASD相关语言模式的准确性和效率.
- 识别出的语言特征可以为ASD患者制定个性化治疗策略.
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