心理健康领域的教学式AI：用于自动化临床监督与风险分诊的三通路微调大语言模型框架
Pedagogical AI in Mental Health: A Tri-Stream Fine-Tuned LLM Framework for Automated Clinical Supervision and Risk Triage
- Microsoft（微软）
- BigCommerce
- University at Buffalo, The State University of New York（纽约州立大学布法罗分校）
- Amazon（亚马逊）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对精神医疗的监督缺口，提出三通路微调Mistral-7B-instruct框架，实现自动化临床监督与风险分诊，提升效率并解决冷启动问题。
AI中文摘要：
现代精神医疗面临资深监督的严重短缺，形成“监督缺口”，新手治疗师在专业反馈延迟的情况下处理高风险问题。本文提出一种新框架，采用微调后的Mistral-7B-instruct模型作为自动化“循环内监督者”系统，利用DAIC-WOZ数据集中的106次会话，执行三通路分析：(1)通过语义一致性追踪治疗联盟；(2)利用注意力加权分析进行潜在风险预测；(3)通过动态临床紧急指数（D-CUI）进行监督分诊。我们的多模态VAL（视觉-声学-语言学）框架实现了95%的技术识别准确率[95%置信区间：75.1%-99.9%]，在5分制量表上的联盟评估平均绝对误差（MAE）为0.105[95%置信区间：0.059-0.151]，治疗保真度α=0.423，平均D-CUI为0.370[95%置信区间：0.322-0.419]。该模型在单个Tesla T4 GPU上训练105步后收敛，损失降低85.2%，将监督分诊延迟从72小时缩短至实时（每次会话约10秒），支持高风险病例的主动干预；同时通过贝叶斯先验解决冷启动问题，采用基于时间戳的模态同步实现稳健的多模态融合。
英文摘要：
Modern mental healthcare faces a critical shortage of senior supervisory oversight, leading to a "supervision gap" where novice therapists manage high-stakes risks with delayed professional feedback. This paper proposes a new framework utilizing a fine-tuned Mistral-7B-instruct model as an automated "Supervisor-in-the-Loop" system. By leveraging 106 sessions from the DAIC-WOZ dataset, the model performs a tri-stream analysis: (1) Therapeutic Alliance tracking via semantic adherence, (2) Latent risk prediction using attention-weighted analytics, and (3) Supervisory Triage via a Dynamic Clinical Urgency Index (D-CUI). Our multi-modal VAL (Visual-Acoustic-Linguistic) framework achieves 95% technique identification accuracy [95% CI: 75.1%-99.9%], alliance assessment MAE of 0.105 on a 5-point scale [95% CI: 0.059-0.151], therapeutic fidelity alpha = 0.423, and mean D-CUI of 0.370 [95% CI: 0.322-0.419]. Training converged in 105 steps with 85.2% loss reduction on a single Tesla T4 GPU. The system reduces supervisory triage latency from 72 hours to real time (~10 seconds per session), enabling proactive intervention in high-risk cases. The system addresses the cold-start problem through Bayesian priors and implements timestamp-based modality synchronization for robust multi-modal fusion.
