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大型语言模型可以从自我报告中提高对PTSD标准A的评估吗?

Mikael Rubin¹, Kayleigh Watters¹, Kaitlin Snyder¹

¹Department of Psychology, Palo Alto University.

Psychological trauma : theory, research, practice and policy

|December 1, 2025

概括

大型语言模型 (LLM) 通过增加参与者报告来改善创伤后应激障碍 (PTSD) 标准A的在线评估. 然而,LLM的准确性与临床医生的评估不匹配,这表明需要进一步研究.

科学领域:

精神病学和心理健康精神病学和心理健康
医疗保健中的人工智能
临床心理学临床心理学

背景情况:

根据DSM-5,对标准A的准确评估对于诊断创伤后应激障碍 (PTSD) 至关重要.
目前在线自我报告方法对标准A评估显示变化,影响诊断一致性.
仅仅依靠自我评估可能不足以进行全面的创伤评估.

研究的目的:

评估大型语言模型 (LLM) 整合到在线自我报告数据收集中,以加强PTSD标准A评估.
将LLM辅助的标准A评估与传统的临床医生评级的表现进行比较.

主要方法:

一项涉及110名成年人的研究使用了在线调查 (Qualtrics) 和通过API集成的LLM,以促使参与者详细描述创伤.
LLM编码了文本描述,并提供了后续问题,以加强数据收集.
四位独立的临床评级人员评估了文本描述,以确定标准A的状态,并通过LLMs评估评级者之间的可靠性.

主要成果:

临床医生评级的标准A评估从65%增加到86%的LLM驱动的后续问题,表明信息捕获的改善.
在LLM和临床医生评级者之间,评级者之间的可靠性是公平的 (原始LLM κ=0.26,较新的LLM κ=0.35).

结论:

将LLM整合到在线评估中增加了参与者报告和满足标准A的事件数量.
目前,LLM无法达到临床医生在A标准上的诊断确定水平.
需要进一步的研究来完善在线心理健康研究和治疗的LLM整合.

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