以LLM为驱动的合作框架,用于知识增强的癌症疼痛评估和管理
Haixiao Liu1, Yue Hu1, Dongtao Li1
1Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
NPJ digital medicine
|January 19, 2026
概括
使用大语言模型 (LLM) 的新框架OncoPainBot通过模拟临床专家来增强癌症疼痛评估和管理. 它在止痛药推方面表现出很高的准确性,改善了个性化的患者护理.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 由于其多方面的机制,可变的阿片类药物反应和不良反应,癌症疼痛带来了复杂的挑战.
- 目前的管理策略需要改进,以便全面评估和个性化处理.
研究的目的:
- 开发和评估OncoPainBot,一个使用大语言模型 (LLM) 进行高级癌症疼痛评估和管理的框架.
- 为了确定临床应用的最佳LLM和检索增强生成 (RAG) 配置.
主要方法:
- 开发了OncoPainBot,整合了四种专门的代理:疼痛提取,疼痛机制推理,治疗计划和安全检查.
- 对比了7个LLM和3个RAG策略,以确定最佳的模型配置.
- 验证了516个癌症疼痛的现实世界电子医疗记录的框架.
主要成果:
- 克劳德-4与RAG相结合,表现出卓越的性能,表现出强大的语义一致性和基于证据的推理.
- 在止痛药推中,OncoPainBot实现了与临床文档的高度一致性和0.841的决策准确性.
- 错误分析表明可靠性很高,差异主要是由于患者特定的因素,而不是错误的药物选择.
结论:
- OncoPainBot证明了基于LLM的系统对癌症疼痛管理的可行性.
- 该框架为个性化止痛护理提供了一种透明,基于证据和临床相关的方法.
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