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在临床面试中检测PTSD:NLP方法和大型语言模型的比较分析

Feng Chen¹, Dror Ben-Zeev², Gillian Sparks³

¹Department of Biomedical Informatics and Health Education, University of Washington, Box 358047 Seattle, WA 98195, USA2Behavioral Research in Technology and Engineering (BRiTE) Center, Department of Psychiatry and Behavioral Sciences, University of Washington, 3751 W Stevens Wy NE Seattle, WA 98195, USA, fengc9@uw.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 27, 2026

概括

使用自然语言处理自动检测创伤后应激障碍 (PTSD) 是有前途的. 基于嵌入的方法,如SentenceBERT,在从临床采访中对PTSD进行分类时取得了最高的准确性.

科学领域:

计算语言学计算语言学
临床心理学临床心理学
人工智能的人工智能

背景情况:

创伤后应激障碍 (PTSD) 在临床环境中经常被发现不足.
自动检测方法为识别有风险的个人提供了潜在的解决方案.
临床面试成绩单是PTSD评估的丰富数据来源.

研究的目的:

评估各种自然语言处理 (NLP) 对二进制PTSD分类的方法.
为了比较基于嵌入的方法,变压器模型和大型语言模型 (LLM) 提示策略.
在DAIC-WOZ数据集上评估这些方法的性能.

主要方法:

使用了DAIC-WOZ数据集,其中包含半结构面试和心理评估.
将SentenceBERT/LLaMA嵌入式与后勤回归进行比较.
评估的一般 (BERT/RoBERTa) 和特定于心理健康的变压器模型.
评估了LLM提示策略,包括零射击,少数射击和思维链.

主要成果:

使用后勤回归的SentenceBERT嵌入实现了最高的性能 (AUPRC=0.758±0.128).
这种方法的性能优于诸如Mental-RoBERTa (AUPRC=0.675±0.084) 等特定领域的模型.
短暂的LLM提示也显示了具有竞争力的结果 (AUPRC=0.737).
严重的PTSD病例和伴随性抑郁症患者的表现更高.

结论:

基于嵌入的NLP方法显示了可扩展的PTSD查的巨大潜力.
在临床环境中,LLM为自动化PTSD检测提供了一种可行的方法.
需要进一步的研究来改善检测微妙的PTSD表现和并发症.

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