一个确定性的大语言模型 (LLM) 框架,用于安全的,符合协议的临床决策支持:在血液透析贫血管理中的应用 (AnemiaCare HDs)
Jose Arriola-Montenegro1, Charat Thongprayoon1, Benjamin Bizer1
1Department of Internal Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States.
Frontiers in artificial intelligence
|December 29, 2025
概括
确定性大语言模型 (LLM) 确保对血液透析患者贫血管理的协议遵守. AnemiaCare HD实现了100%的准确性,改善了临床决策支持的安全性和监管调整.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 腎臟病學 (nephrology) 是一種醫學專業.
背景情况:
- 大型语言模型 (LLM) 提供了临床决策支持的潜力,但在高风险领域的协议遵守和安全性方面面临挑战,例如血液透析患者的贫血管理.
- 严格遵守红色形成刺激剂 (ESA) 和静脉注射 (IV) 铁剂量协议对于在血液透析中安全有效地治疗贫血至关重要.
- 无法控制的LLM行为在临床环境中构成重大安全和监管风险.
研究的目的:
- 开发和评估一个确定性的大语言模型 (LLM) 框架,AnemiaCare HD,旨在在贫血管理中提供透明,可重复和符合协议的临床建议.
- 解决与在高风险临床决策支持场景中使用LLM相关的安全和监管问题.
主要方法:
- 在600例模拟血液透析贫血病例中,AnemiaCare HD基于标准化的机构协议进行了测试.
- 该模型使用了六个固定的临床输入:血红蛋白,血红蛋白变化速率,趋势方向,转林和度,费里丁和当前ESA剂量.
- 进行了两个阶段的评估:第一阶段使用了松散结构的提示,而第二阶段使用了具有嵌入式安全功能和ESA/铁剂量规则的确定性提示逻辑. 两个独立的脏病学家评估了协议的遵守情况.
主要成果:
- 第1阶段 (松散结构提示) 显示只有32%的协议遵守 (96/300例),在ESA定位,铁剂量和时间上经常出现错误.
- 第二阶段 (决定性提示) 实现了100%的协议遵守 (300/300例),消除了所有偏差,不安全的铁剂量和时间违规.
- 与第一阶段相比,第二阶段的确定性编码显著提高了安全性和一致性 (p < 0.001),产生了结构化,基于逻辑的输出.
结论:
- 确定性LLM工程对于在高风险的治疗领域创建安全和合规的临床决策支持工具是有效的.
- AnemiaCare HD 证明了为临床应用开发符合监管的,可审计的LLM框架的可行性.
- 进一步的现实世界整合和前性验证是临床采用的下一个关键步骤.
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