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通过大型语言模型加强对医疗产品的营销后监测

Michael E Matheny^1,2,3,4, Jie Yang⁵, Joshua C Smith¹

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.

JAMA network open

|August 16, 2024

概括

大型语言模型 (LLM) 通过分析健康记录,为改善药物安全监测提供了新的途径. 需要仔细实施,以确保准确性,公平性,并避免在营销后安全评估中的错误信号.

科学领域:

药监和药物安全药监和药物安全
医疗信息学医疗信息学
医疗保健中的人工智能

背景情况:

作为FDA的一个关键倡议,Sentinel系统使用临床数据进行营销后药物安全监测.
目前观察数据的局限性阻碍了可靠的安全问题评估.
大型语言模型 (LLM) 的进步为解决这些局限性提供了机会.

研究的目的:

探索LLMs在加强营销后药物安全监测方面的潜力.
确定LLM如何支持信号识别和流行病学调查.
解决在监管环境中有效部署LLM的挑战和考虑.

主要方法:

利用LLM从电子健康记录叙述中进行信号识别.
在流行病学研究中使用LLM进行概率表型和混因子提取.
利用LLM来从多种生物医学知识来源中提取信息.

主要成果:

法律法规可以识别新的不良事件信号,并支持因果关系调查.
在传统的自然语言处理之外,LLM提供了增强的信息提取能力.
挑战包括对准确性的快速工程,基础设施要求,以及产生非事实陈述的可能性.

结论:

法律法规提供了一个新的范式,以推进医疗产品的营销后监管.
需要进一步努力,以确保在信号检测中公平,准确和严格地使用LLM.
解决LLM的局限性对于可靠的监管决策和最大限度地减少误报至关重要.

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