使用推理大语言模型改进药物错误分类
Anders Krifors1,2, Theodor Beskow3, Magnus Jonsson3
1Centre for Clinical Research Västmanland, Uppsala University, Västerås Hospital, 721 89 Västerås, Sweden.
JAMIA open
|January 28, 2026
概括
一个大型语言模型 (LLM) 在识别医疗报告中的药物错误方面展示了专家级的性能,与专家分类达到了96%的一致性. 这种人工智能工具可以通过提高错误检测效率和准确性来提高患者的安全性.
科学领域:
- 医疗保健中的人工智能
- 临床信息学 临床信息学
- 患者安全研究 患者安全研究
背景情况:
- 药物错误对患者安全构成重大威胁.
- 需要自动化方法来识别事故报告中的药物错误,以提高效率和准确性.
- 大型语言模型 (LLM) 显示了分析复杂医学文本的潜力.
研究的目的:
- 评估推理大语言模型 (LLM) 在医疗事故报告中识别药物错误的性能.
- 将LLM的准确性与药剂师的专家分类进行比较.
主要方法:
- OpenAI的O4-mini LLM是通过对75000个匿名事件报告的快速工程进行调整的.
- 药剂师手动重新分类了2,434份报告的子集,以指导快速设计.
- 对200份报告进行了验证,由两名药剂师独立分类.
主要成果:
- 该LLM取得了96.0%的一致率 (192/200) 与专家分类.
- 不同意 (4.0%) 主要是由于语言模两可或上下文.
- 对药物错误的分类准确率为76.5%.
结论:
- 该LLM展示了专家级别的性能,超过了现有的药物错误识别自动化方法.
- 将这种人工智能驱动的方法集成到临床信息学工作流中可以提高患者的安全性.
- 经过验证的AI工具使医疗机构能够快速,一致地识别药物错误.
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