药物不良事件的大型语言模型:临床视角
Md Muntasir Zitu1, Dwight Owen2, Ashish Manne2
1Department of Machine Learning, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
大型语言模型 (LLM) 显示出从临床文本中自动检测不良药物事件 (ADE) 的前景,提高了患者的安全性. 数据隐私和模型可解释性等挑战需要解决,以实现更广泛的采用.
科学领域:
- 药物监测和临床信息学
- 医疗保健中的人工智能
背景情况:
- 药物不良事件 (ADEs) 对患者安全和医疗保健结果构成重大风险.
- 从临床叙述中手动检测ADEs是低效和昂贵的.
- 大型语言模型 (LLM) 为ADE提取提供了自动化解决方案.
研究的目的:
- 审查目前在临床环境中用于ADE检测的LLM的应用.
- 以LLM为基础的ADE检测研究进行分类.
- 确定在药监监管中实施LLM的挑战和未来方向.
主要方法:
- 对39篇文章进行了叙事审查.
- 研究根据在ADE检测中的LLM应用进行了分类.
- 专注于临床决策支持,与免疫相关的ADE,与癌症相关的ADE和个性化系统.
主要成果:
- 在ADE检测方面,LLM驱动的方法表现出强的性能,经常超过传统方法.
- 应用范围包括决策支持工具,监视和个性化医疗.
- 确定的局限性包括域变性,可解释性,数据问题和基础设施需求.
结论:
- 临床临床医学具有显著的潜力,可以提高药物监测和患者安全.
- 解决确定的局限性对于有效的临床工作流集成至关重要.
- 未来的研究应该专注于提高模型的稳定性,可解释性和数据安全性.
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