用理论驱动的生成语言模拟来描述精神病学中语义坚持和脱轨的矛盾相互作用
Robin Quillivic1,2, Raymond J Dolan3,4, Isaac Fradkin5
1CESSP, UMR 8209, Paris 75005, France.
Schizophrenia bulletin
|November 28, 2025
概括
新的自然语言处理 (NLP) 指标有效地捕捉了形式思维障碍 (FTD) 的坚持,将其与脱轨区分开来. 这些基于密度的指标显示出对理解各种精神疾病中的FTD症状的希望.
科学领域:
- 计算语言学计算语言学
- 精神病诊断诊断精神病学诊断的方法
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 正式思维障碍 (FTD),标志着脱轨和坚持不,是精神病的关键,并且存在于其他精神疾病中.
- 现有的FTD的NLP指标,如共弦距离,由于它们的相反效应,难以区分脱轨和坚持.
- 需要新的NLP指标来具体识别和量化坚持,而不依赖于语音失序.
研究的目的:
- 开发和验证新的NLP指标,以准确测量在正式思维障碍中的坚持.
- 使用先进的NLP技术,区分坚持和脱轨的语言标记.
- 评估这些新指标在跨诊断样本和与精神病理维度相关的有用性.
主要方法:
- 使用大型语言模型生成合成叙事数据,独立操纵语义坚持和脱轨.
- 开发了基于密度的新型NLP指标,重点是通过集群和缩小维度的语义结构和密度.
- 新型指标的表现与合成数据上的传统的共弦距离指标和811名参与者的普通人口样本进行了比较.
主要成果:
- 传统的共弦距离度量有效地检测出轨,但对坚持不够准确.
- 基于密度的新型指标在识别和分离从脱轨的坚持方面表现出了卓越的表现.
- 密度指标在识别与重复思维,抑郁症状和负面FTD相关的坚持方面也超过了传统指标.
结论:
- 将基于密度的NLP指标与传统的语义距离测量集成,为捕捉FTD症状提供了更全面的方法.
- 这种新的方法增强了对坚持的理解,以及它与思想和言语失序的相互作用.
- 需要进一步的临床验证,但这些指标有可能改善FTD的临床评估和理解.
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