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通过使用大型语言模型,推进预测和理解慢性背痛中安慰剂反应

Diogo A P Nunes^1,2, Dan Furrer^3,4,5, Sara Berger⁶

¹Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento, Lisbon, Portugal.

European journal of pain (London, England)

|December 12, 2025

概括

大型语言模型 (LLM) 可以预测慢性腰痛患者的安慰剂反应,准确度为74%. 这种数据驱动的方法识别了心理社会因素,如焦虑和希望,提高了我们对安慰剂效应的理解.

科学领域:

计算语言学计算语言学
临床心理学临床心理学
疼痛管理疼痛管理

背景情况:

安慰剂止痛是一种复杂的现象,具有可变的个体反应.
之前对生物心理社会因素的研究受到概括性和生态有效性的限制.

研究的目的:

评估微调的大型语言模型 (LLMs) 用于预测安慰剂反应.
从患者采访中提取上下文特征,以实现数据驱动的方法.

主要方法:

临床医学专家分析了来自慢性腰背痛的两项随机对照试验 (RCT) 的患者访谈.
从治疗后 (发现) 和治疗前 (验证) 进行的采访中提取了特征.

主要成果:

在未见的数据中,LLM预测了安慰剂反应,准确度为74%,在独立队列中为70%.
通过LLM,实现了完全数据驱动的方法,消除了预先选择的搜索术语的需求.
提供了可解释的心理社会因素 (如焦虑,希望) 的洞察力,这些因素是安慰剂反应的基础.

结论:

最先进的NLP技术解决了解释性和上下文敏感性的局限性.
在生物心理社会现象的定量探索中,LLMs将语言和心理状态联系起来.
这种方法增强了对语言与治疗结果,特别是安慰剂效应的关系的理解.

关键词:

慢性疼痛是一种慢性疼痛.语言语言语言语言语言语言.大型语言模型.机器学习是机器学习.果果果果

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