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利用大型语言模型来诊断与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, NY, USA.

NPJ digital medicine

|December 16, 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. 这种人工智能方法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.
现有的对自闭症相关语言障碍的评估工具往往缺乏速度和精度.
传统评估的变化可能会阻碍自闭症的准确和及时诊断.

研究的目的:

探索大型语言模型 (LLM) 的使用,以提高与自闭症相关的语言障碍的诊断.
通过利用自然语言理解 (NLU) 来提高自闭症诊断的灵敏度和精度.
识别和描述关键的语言特征,表明自闭症谱系障碍.

主要方法:

利用具有自然语言理解 (NLU) 能力的大型语言模型 (LLM).
采用零射击学习配置来评估模型性能,而无需对数据集进行先前的具体培训.
在语言样本中分析语言特征,以确定与自闭症相关的模式.

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.
该框架证明了作为补充诊断工具的提高准确性和适用性.
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提供了一种有希望的方法来克服诊断自闭症相关语言障碍的局限性.
开发的框架可以作为临床医生有价值的补充工具.
识别特定的语言特征有助于更精确的诊断和个性化治疗ASD.

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