个性化的rTMS治疗建议与提取增强的LLM推理.
Lingling Xu1,2, Haoran Xie3, Xiaohui Tao4
1Division of Artificial Intelligence, School of Data Science, Lingnan University, Hong Kong SAR, China.
Brain informatics
|November 7, 2025
概括
这项研究引入了一个新的AI框架,用于个性化的重复性跨磁刺激 (rTMS) 来治疗抑郁症. 该系统使用大型语言模型将rTMS协议定制为个别患者的需求,提高治疗效率.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 临床精神病学 临床精神病学
背景情况:
- 重复性横磁刺激 (rTMS) 是主要抑郁障碍 (MDD) 和耐治疗抑郁症 (TRD) 的关键治疗方法.
- 当前的rTMS协议往往缺乏个性化,无法解决个体患者的变化.
- 需要基于数据的方法来优化rTMS治疗参数.
研究的目的:
- 开发和评估一个新的,可解释的框架,以提供个性化的rTMS治疗建议.
- 整合患者个人资料和临床数据,用于定制的rTMS协议生成.
- 通过个性化治疗策略,提高rTMS治疗的疗效.
主要方法:
- 一个检索增强生成 (RAG) 框架,结合了句子嵌入模型和大语言模型 (LLM).
- 患者的个人资料被编码为语义表示,用于检索类似的临床病例.
- LLM使用检索的案例来进行短暂的上下文学习,以合成个性化的rTMS参数 (频率,强度,模式).
主要成果:
- 该框架使用Bge-large-en-v1.5进行检索和GPT-4o-mini进行推理,并使用15个短暂的示例实现了78.18%的高RTMS协议匹配精度.
- 该方法成功地集成了多个rTMS参数,以实现全面的个性化.
- 该系统展示了可解释性和无微调操作.
结论:
- 拟议的人工智能框架提供了一个有希望的,数据驱动的方法来个性化rTMS治疗抑郁症.
- 这种方法通过考虑个体患者的特征来提高治疗定制性.
- 该框架可适应资源贫困的临床环境,推进神经刺激疗法.
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