通过分析语音对话中的情绪变化，预测抑郁焦虑治疗效果。
Language Markers of Emotion Flexibility Predict Depression and Anxiety Treatment Outcomes
- 用小语言模型提取每轮对话的情绪，构建动态情绪网络。
- 非应答者情绪以悲伤和恐惧为主，改善者情绪更平衡。
- 为临床风险分层提供可解释的自动化指标，适合心理治疗研究者。
预测焦虑和抑郁症治疗无效具有挑战性，部分原因在于真实医疗环境中症状评估稀疏。本研究分析了12,043名美国中重度焦虑与抑郁患者在12周内的去标识化远程治疗对话记录，探索被动捕捉的细粒度情绪是否可作为治疗结果的语言标志。采用基于Transformer的小型语言模型，在话语轮次层面提取患者情绪；使用状态空间模型（VISTA-SSM）基于情绪动态聚类子群体，并生成时间网络。结果显示两类群体：改善组（n=8,230）与非应答组（n=3,813），后者表现出更高症状恶化风险及更低临床显著改善可能性。时间网络显示，非应答者情绪动态受悲伤和恐惧主导，而改善者则表现出快乐、悲伤与中性情绪的平衡。研究提示，情绪灵活性的语言标志可作为可扩展、可解释且理论基础扎实的治疗风险分层指标。
原文摘要 · Abstract (English)
Predicting treatment non-response for anxiety and depression is challenging, in part because of sparse symptom assessments in real-world care. We examined whether passively captured, fine-grained emotions serve as linguistic markers of treatment outcomes by analyzing 12 weeks of de-identified teletherapy transcripts from 12,043 U.S. patients with moderate-to-severe anxiety and depression symptoms. A transformer-based small language model extracted patients' emotions at the talk-turn level; a state-space model (VISTA-SSM) clustered subgroups based on emotion dynamics over time and produced temporal networks. Two groups emerged: an improving group (n=8,230) and a non-response group (n=3,813) showing increased odds of symptom deterioration, and lower likelihood of clinically significant improvement. Temporal networks indicated that sadness and fear exerted most influence on emotion dynamics in non-responders, whereas improving patients showed balanced joy, sadness, and neutral expressions. Findings suggest that linguistic markers of emotional inflexibility can serve as scalable, interpretable, and theoretically grounded indicators for treatment risk stratification.
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