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使用大型语言模型作为可扩展的心理状态评估技术

Margot Wagner¹, Callum Stephenson², Jasleen Jagayat²

¹Institute for Neural Computation, University of California San Diego, San Diego, CA, USA. mwagner@ucsd.edu.

NPP - digital psychiatry and neuroscience

|December 8, 2025

概括

大型语言模型现在可以从文本中评估心理健康状况. 这种人工智能方法在识别焦虑和抑郁症的症状方面表现出与人类专家相提并论的准确性.

科学领域:

人工智能的人工智能
自然语言处理自然语言处理.
心理健康技术心理健康技术

背景情况:

在心理健康护理需求和服务可用性之间存在很大的差距.
客观和可扩展的心理健康评估工具至关重要.
口语和书面语言是精神健康诊断和治疗的组成部分.

研究的目的:

研究大语言模型 (LLM) 在心理健康状况评估中的有效性.
开发和微调基于RoBERTa的变压器模型,用于分析书面语言.
通过使用人工智能驱动的工具来弥合心理健康服务可用性的差距.

主要方法:

使用自然语言处理微调基于RoBERTa的变压器模型.
利用非临床在线论坛数据和来自在线心理治疗试验的临床数据.
通过反向翻译增强扩展文本数据集,并通过超参数调整优化性能.

主要成果:

微调的LLM证明了分析书面语言以识别心理健康症状的能力.
在对症状性句子进行分类时,模型的准确性与人类专家的评估相当.
该模型在识别表明焦虑或抑郁的句子时实现了74%的预测准确度.

结论:

作为心理健康评估的可扩展工具,LLM表现有前途.
通过人工智能对书面文本的分析,可以支持对心理健康症状的临床识别.
这种方法提供了一种增强现有的心理健康诊断能力的潜在方法.

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