使用Livertox数据开发基于药物诱导的肝损伤的取回增强生成的大型语言模型
Ashwin Rao1, George Cholankeril1, Avegail Flores1
1Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
Hepatology communications
|February 12, 2026
概括
使用LiverTox数据的检索增强生成 (RAG) 提高了药物诱导肝损伤 (DILI) 决策支持的大型语言模型 (LLM) 的准确性. RAG-LLMs为临床使用提供可靠,无幻觉的答案.
科学领域:
- 生物医学信息学 生物医学信息学
- 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
背景情况:
- 药物诱导的奇异性肝损伤 (DILI) 是一个重大的临床挑战,需要准确的决策支持.
- 像LiverTox这样的现有资源是全面的,但并没有优化为点的护理使用.
- 一般的大型语言模型 (LLM) 由于潜在的幻觉和错误信息而带来安全风险.
研究的目的:
- 评估检索增强生成 (RAG) 的有效性,以提高DILI信息检索的LLM准确性.
- 确定RAG集成是否可以减轻LLM幻觉并改善临床决策支持.
- 通过使用LiverTox数据,评估RAG-LLM与非RAGLLM的性能.
主要方法:
- 通过使用BioBERT嵌入,将1343个LiverTox药物专著编制成8759个索引细分.
- 开发了一个RAG管道,具有药物特定优先级,部分意识权重和语义搜索.
- 在4个RAG-LLM (Mistral-7B,Claude-3-Haiku,Claude-3-Opus,GPT-4o) 和2个非RAG GPT-4o变体中评估了25个DILI问题.
- 肝病学家使用利克特尺度评估了回应的准确性,完整性和简洁性.
主要成果:
- 在准确性 (p<0.001) 和完整性 (p<0.01) 方面,RAG-LLMs显著优于非RAG GPT-4o变体.
- GPT-4o (RAG) 获得了最高的总分 (4.47±0.10).
- 在RAG-LLM输出中没有观察到幻觉,而非RAG变体产生了几个幻觉反应.
结论:
- 与LiverTox集成的RAG-LLM提供基于证据的DILI管理支持.
- 与非RAG变种相比,RAG-LLM系统表现出更高的性能和可靠性.
- 开发的LiverTox RAG-LLM提供了一个可靠的工具,对药物肝毒性提供可靠的治疗点答案.
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