语言还是综合症? 通过大型语言模型嵌入来调查一般精神病理因素的起源
Hiroki Kojima1, Takafumi Soda1, Yuichi Yamashita1
1Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan.
Psychiatry and clinical neurosciences
|December 23, 2025
概括
大型语言模型 (LLM) 显示,症状语言反映了一般精神病理因素 (p-factor). 然而,现实世界症状的同时出现提供了额外的临床相关信息,超出了语义相似性.
科学领域:
- 精神病学是一个精神病学.
- 计算语言学 计算语言学
- 心理测量 心理测量 心理测量
背景情况:
- 精神病诊断面临的挑战来自并发症和症状重叠.
- 一般精神病理因子 (p-因子) 是一个跨诊断层面的兴趣.
- 区分p因子的语义基础与真正的症状共发生至关重要.
研究的目的:
- 调查p因子是否反映了语义重叠或综合征共发生.
- 利用大型语言模型 (LLM) 来将语言相似性与症状共发生脱而出.
- 评估语义与综合征p因子表示的临床有效性.
主要方法:
- 分析了145个症状项目,使用LLM嵌入用于语义相似性和综合征共发生的多色相关性.
- 采用探索性因子分析,从两种相似性矩阵中提取潜在结构.
- 量化了语义和综合症因子结构的对齐,并测试了它们对终身诊断的预测有效性.
主要成果:
- 语义和综合症分析都确定了具有高交叉诊断负载的主导p因子.
- 从LLM获得的语义相似性在很大程度上回顾了人类响应数据中发现的潜在结构.
- 与语义分数相比,综合征p因子得分证明了对个体诊断史的优越预测.
结论:
- 在LLM嵌入式中,在症状项目语言中捕获p因子的语义表示.
- 现实世界症状的同时出现包含了独特的,临床相关的信息,这些信息不能仅仅通过语义来捕获.
- 基于LLM的分析为精神病学构造研究提供了一种新的方法,突出了语言和临床现象之间的相互作用.
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