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解释心理治疗成绩单中的焦虑预测:患者语言特征和理论构造的作用

Tobias Steinbrenner¹, Christopher Lalk¹, Kim Targan¹

¹Department of Clinical Psychology and Psychotherapy of Adulthood, Osnabrück University, Lise-Meitner-Str. 3, 49069 Osnabrück, Germany.

Behaviour research and therapy

|September 20, 2025

概括

这项研究使用患者语言从治疗成绩单中预测焦虑的严重程度. 机器学习模型识别了语言特征,唯一图形和大图形显示了最高的准确性,而基于理论的模型提供了可解释的见解.

科学领域:

心理语言学心理语言学
计算语言学计算语言学
临床心理学临床心理学

背景情况:

语言学提供了对心理过程和症状的洞察.
以前的研究往往侧重于孤立的语言特征.
将心理构造与NLP相结合,可以提高预测和解释能力.

研究的目的:

从心理治疗成绩单中的患者语言中预测焦虑的严重程度.
比较不同机器学习模型的性能.
确定预测焦虑的最重要的语言特征.

主要方法:

从529个转录中提取了LIWC特征,唯一图,大图,变压器情绪和主题.
开发了理论驱动的特征,用于负面的自我关注,自我洞察力,威胁感知和不确定性.
应用机器学习算法和可解释的人工智能 (XAI) 用于特征重要性分析.

主要成果:

尤尼格拉姆-比格拉姆模型实现了最高的预测性能 (r = .77).
焦虑过程模型表现出了显著的准确性,具有可解释的,基于理论的特征.
与社会关系和不安全感相关的语言特征与较低的焦虑相关,而健康和确定性词与更高的焦虑相关.

结论:

在语言分析中,预测性能和可解释性之间存在一个权衡.
联合格拉姆-比格拉姆模型在准确性方面表现出色,而理论驱动的模型提供了临床见解.
预测性语言特征,特别是那些与心理理论一致的特征,可以告知反系统和临床应用.

关键词:

贝尔主题贝尔主题贝尔主题这里是LIWCWCWC.机器学习机器学习心理治疗的研究研究.变压器变压器变压器焦虑是一种焦虑.

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