使用基于推理的大型语言模型预测抑郁症患者12周缓解期:模型开发和验证研究
Jin-Hyun Park1, Hee-Ju Kang2, Ji Hyeon Jeon2
1Department of Biomedical Informatics, Korea University College of Medicine, 161, Jeongneung-ro, Seongbuk-gu, Seoul, 02708, Republic of Korea, 82 2-3407-2099.
JMIR mental health
|January 23, 2026
概括
大型语言模型 (LLM) 在预测抑郁障碍抗抑郁药治疗反应方面表现有前途. 这些人工智能工具可以帮助识别可能对药物反应的患者,帮助临床决策.
科学领域:
- 人工智能在心理健康中的作用
- 计算精神病学是一种计算精神病学.
- 数字治疗学数字治疗学
背景情况:
- 抑郁症影响全球超过3亿人,在初始抗抑郁药单一治疗时,缓解率有限 (30-40%).
- 对于预测抑郁症管理中的早期治疗反应的数字工具,存在重大未满足的需求.
研究的目的:
- 评估基于推理的大型语言模型 (LLM) 在预测抗抑郁药单一治疗抑郁症患者12周缓解的准确性.
- 评估LLM生成的理由的临床有效性和可解释性,以整合到数字心理健康工作流程中.
主要方法:
- 分析了MAKE生物标志物发现研究中的390名患者的数据,这些患者正在接受第一阶段抗抑郁药单一治疗.
- 测试了三个LLM (ChatGPT o1,o3-mini,Claude 3.7 Sonnet) 使用先进的提示策略,包括引用深度研究.
- 用平衡的准确性,灵敏性,特异性,PPV和NPV评估模型性能,并由三个精神病学家进行独立的临床有效性评估.
主要成果:
- 克劳德3.7索内特通过一种新的提示策略实现了最高的性能 (平衡精度=0.6697,灵敏度=0.7183,特异性=0.6210).
- 主要抗抑郁药的高负预测值 (≥0.75) 表明在识别可能的非响应者方面具有实用性.
- 精神科医生对LLM的正确性 (4.3/5),一致性 (4.2/5),和帮助性 (4.2/5) 的结果进行了有利的评价.
结论:
- 基于推理的LLM,特别是以研究为基础的提示,显示出预测抑郁障碍中抗抑郁药物反应的潜力.
- 这些人工智能工具可以在治疗规划中作为可解释的辅助工具.
- 在现实世界的临床环境中进行前性验证对于广泛采用至关重要.
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