大型语言模型和文本嵌入用于检测患者叙述中的抑郁症和自杀
Silvia Kyungjin Lho1, Sang-Cheol Park2, Hahyun Lee1,3
1Department of Psychiatry, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea.
JAMA network open
|May 23, 2025
概括
大型语言模型 (LLM) 和文本嵌入模型在从患者叙述中识别抑郁症和自杀风险方面表现有前途. 自我概念叙述对检测最有效,尽管临床应用需要进一步细化模型.
科学领域:
- 人工智能在心理健康中的作用
- 用于临床评估的自然语言处理.
- 计算精神病学是一种计算精神病学.
背景情况:
- 大型语言模型 (LLM) 和文本嵌入模型为分析心理健康中叙事数据提供了新的方法.
- 以前的研究表明,人工智能在评估精神病患者风险方面具有潜力.
研究的目的:
- 用句子完成测试 (SCT) 叙述来评估LLM和文本嵌入模型在检测抑郁症和自杀风险方面的有效性.
- 为了比较不同LLM和文本嵌入模型在识别心理健康风险方面的表现.
主要方法:
- 一项横截面研究分析了来自1064名精神病患者 (18-39岁) 的SCT数据.
- 在LLM (GPT-4o,Gemini-1.0-pro,GPT-3.5-turbo-16k) 和文本嵌入模型 (text-embedding-3-large, -small, ada-002) 中处理了SCT叙述.
- 使用AUROC,平衡准确度和宏观F1得分来衡量表现,重点关注自我概念,家庭,性别认知和人际关系叙述.
主要成果:
- 使用自我概念叙述,LLM1在抑郁症 (AUROC 0.720) 和自杀风险 (AUROC 0.731) 上表现出强大的零射击性能.
- 短暂的学习提高了LLM的性能;文本嵌入-3-large与极端梯度提升实现了抑郁症 (0.841) 的最高AUROC.
- 自我概念叙述在所有评估模型中产生了最准确的风险检测.
结论:
- 通过SCT叙述,LLM和文本嵌入模型显示了通过SCT叙述,特别是自我概念数据来检测精神病患者的抑郁症和自杀风险的潜力.
- 虽然有希望,但模型性能和安全性的进一步进步对于安全的临床实施是必要的.
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