使用Bert主题建模测量心理治疗成绩单中的联盟和症状严重程度
Christopher Lalk1, Tobias Steinbrenner2, Weronika Kania2
1Department of Psychology, Osnabrück University, Osnabrück, Germany. Christopher.lalk@uni-osnabrueck.de.
Administration and policy in mental health
|March 29, 2024
概括
主题建模准确地从患者和治疗师的成绩单预测心理治疗的结果. 患者的语言主题预测症状严重程度,而治疗师的语言主题预测治疗联盟,揭示治疗改进的关键主题.
科学领域:
- 心理学 心理学 心理学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 心理治疗研究越来越多地利用计算方法来分析会话记录.
- 主题建模提供了一种新的方法来发现潜在的主题和预测临床结果.
研究的目的:
- 将主题建模应用于心理治疗成绩单,以预测症状严重程度和治疗结盟.
- 通过使用可解释AI (XAI) 来识别推动这些预测的关键主题和主题.
主要方法:
- 分析了来自124名患者的552份心理治疗成绩单,其中124名患者使用BERTopic进行话题提取.
- 机器学习模型用于从提取的患者和治疗师主题中预测症状严重程度和联盟.
- 用于识别和解释重要主题和主题的定性分析和XAI.
主要成果:
- 患者主题显著预测了症状严重程度 (R2=0.45),健康和负面经历的主题是关键驱动因素.
- 治疗师主题显著预测了治疗联盟 (R2=0.20),与心理治疗框架,收入和日常生活相关的主题与较低的联盟有关.
- 灵敏度分析证实了不同建模方法的发现的稳定性.
结论:
- 主题建模在心理治疗研究中显示出预测治疗相关指标的巨大潜力.
- 通过详细说明特定主题和主题的预测价值,XAI提高了可解释性.
- 研究结果强调了患者和治疗师对治疗结果的言论的独特贡献.
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