用NLP自动分析心理治疗对话，识别患者依恋类型。
The Language of Attachment: Modeling Attachment Dynamics in Psychotherapy
- 用NLP分类模型从治疗对话中自动识别依恋风格。
- 发现误判‘焦虑型’为‘回避型’对治疗效果影响更严重。
- 为个性化心理治疗和机制研究提供新工具，适合临床AI方向读者。
通过自然语言处理（NLP）改进心理治疗中的心理健康服务，特别是自动识别患者个体特征如依恋风格。当前依恋风格评估依赖人工，使用患者依恋编码系统（PACS；Talia et al., 2017），过程复杂、耗时且需长期培训。为推动依恋导向治疗与研究的广泛应用，我们首次探索利用NLP分类模型从心理治疗转录文本中自动评估依恋风格。我们进一步分析结果并讨论自动化工具的应用影响——例如，将‘焦虑型’患者误判为‘回避型’可能比其他误判对治疗结果造成更大负面影响。本研究开辟了通过NLP进步实现更个性化心理治疗及深入探究治疗机制的新路径。
原文摘要 · Abstract (English)
The delivery of mental healthcare through psychotherapy stands to benefit immensely from developments within Natural Language Processing (NLP), in particular through the automatic identification of patient specific qualities, such as attachment style. Currently, the assessment of attachment style is performed manually using the Patient Attachment Coding System (PACS; Talia et al., 2017), which is complex, resource-consuming and requires extensive training. To enable wide and scalable adoption of attachment informed treatment and research, we propose the first exploratory analysis into automatically assessing patient attachment style from psychotherapy transcripts using NLP classification models. We further analyze the results and discuss the implications of using automated tools for this purpose -- e.g., confusing `preoccupied' patients with `avoidant' likely has a more negative impact on therapy outcomes with respect to other mislabeling. Our work opens an avenue of research enabling more personalized psychotherapy and more targeted research into the mechanisms of psychotherapy through advancements in NLP.
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