自然语言处理模型用于预测互联网提供的认知行为治疗中的治疗结果
Nils Hentati Isacsson1, Lucía Gómez-Zaragozá1,2, Fehmi Ben Abdesslem1,3,4
1Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Sweden.
Internet interventions
|October 27, 2025
概括
在互联网交付的认知行为疗法 (ICBT) 中分析患者-治疗师信息的自然语言处理 (NLP) 模型显示治疗结果的预测价值有限. 仅仅症状是比NLP对文本交互的分析更好的预测因素.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 通过预测治疗结果,可以提高通过互联网提供的认知行为疗法 (ICBT) 的有效性.
- 通过书面信息进行患者-治疗师沟通是指导性ICBT的关键组成部分.
- 自然语言处理 (NLP) 提供了分析这些信息以预测结果的潜力,但证据有限.
研究的目的:
- 调查NLP模型对ICBT治疗结果的预测准确度.
- 评估NLP对患者-治疗师信息的分析是否提供了超出症状变量之外的额外预测价值.
主要方法:
- 训练了三个NLP模型 (TF-IDF,BERT,BELT) 在一个为期12周的ICBT计划中的6613个患者-治疗师信息上.
- 模型被训练有或没有初始症状变量来预测治疗后症状.
- 将NLP模型与虚拟模型和仅使用症状数据的线性回归模型进行比较. 采用多重归算和嵌套交叉验证.
主要成果:
- 仅使用症状变量的线性回归模型表现最好,优于NLP模型.
- 伯特模型显示,比模拟模型略有改善 (RMSE 0.17对比 0.18).
- 将BERT与症状变量相结合提高了准确性,但没有RMSE;仅症状模型的BACC (70%) 和F1得分 (0.66) 比NLP模型更高.
结论:
- 在ICBT中对患者-治疗师信息的NLP分析对治疗结果的预测价值有限.
- 除了单独使用症状变量之外,NLP没有增加显著的预测价值.
- 需要进一步的研究来完善用于心理治疗的NLP方法,并评估ICBT中基于文本的交互潜力.
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