使用大型语言模型识别药物相互作用
Kaitlin Blotske1, Xingmeng Zhao1, Kelli Henry1
1University of Colorado School of Medicine, Department of Biomedical Informatics.
medRxiv : the preprint server for health sciences
|January 8, 2026
概括
大型语言模型 (LLM) 显示了识别药物相互作用 (DDI) 的潜力,但性能因任务复杂性而异. 随着推理的增加,可靠性下降,需要对药物安全性进行仔细评估.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 人工智能在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 药物相互作用 (DDI) 是患者损害的主要原因,特别是多种药物.
- 目前的电子健康记录 (EHR) 系统使用基于规则的软件来检测DDI.
- 大型语言模型 (LLM) 为DDI识别提供了潜力,但需要严格的验证.
研究的目的:
- 为了比较LLM在识别和管理DDI方面的表现.
- 开发和使用临床医生注释的数据集来评估LLM DDI检测能力.
- 评估跨各种任务复杂性和相互作用严重性的LLM绩效.
主要方法:
- 在750场景的DDI数据集上评估了三个LLM (GPT-4o-mini,MedGemma-27B,LLaMA3-70B).
- 使用了三种任务格式:两种药物分类,三种药物歧视和4-6种药物选择.
- 使用精度,回忆,F1得分,准确性,自我一致性和与信心一致的指标来评估性能.
主要成果:
- LLaMA3-70B在两种药物分类召回和F1得分方面表现出色.
- GPT-4o-mini在多种药物任务中表现出卓越的准确性和一致性.
- 随着任务复杂度的增加,模型的自我一致性和可靠性下降.
结论:
- 在复杂的任务中,LLM显示了DDI识别的可变能力,而复杂任务的性能下降.
- 当前的LLM在不同的推理格式中缺乏统一的可靠性.
- 多格式评估和可靠性意识评估对于安全的药物安全LLM应用至关重要.
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