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AE-GPT:使用大型语言模型从监测报告中提取不良事件 - 用于流感疫苗不良事件的用例

Yiming Li¹, Jianfu Li², Jianping He¹

¹McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States of America.

|March 21, 2024

概括

大型语言模型 (LLM) 在识别疫苗不良事件 (AE) 中表现有希望. 一个微调的GPT 3.5模型在从临床报告中提取AEs方面取得了很高的准确性,从而推进了医疗数据分析.

科学领域:

医疗信息学医学信息学
医疗保健中的人工智能
疫苗安全监督疫苗安全监督

背景情况:

疫苗对全球健康至关重要,但可能导致不良事件 (AE).
准确识别和编目AE对于疫苗安全监测至关重要.
大型语言模型 (LLM) 提供了从临床数据中自动提取AE的潜力.

研究的目的:

评估各种LLM在提取疫苗相关不良事件 (AE) 的能力.
评估精细调整的LLM的性能,专门用于在疫苗安全数据中检测AE.

主要方法:

利用了1990-2016年疫苗不良事件报告系统 (VAERS) 的数据.
评估了多个LLM,包括GPT-2,GPT-3变体,GPT-4和Llama2.
专注于使用流感疫苗数据作为案例研究的不良事件提取.

主要成果:

一个微调的GPT 3.5型号,名为AE-GPT,表现出卓越的性能.
AE-GPT在严格比赛中获得了0.704的微F1平均得分,在放松比赛中获得了0.816的平均得分.
结果强调了LLM在处理AE识别复杂医疗数据方面的有效性.

结论:

LLM,特别是微调的AE-GPT,显示出先进的不良事件检测的巨大潜力.
这种方法在利用人工智能用于疫苗安全性和医学数据分析方面取得了显著的进步.
证明的能力很可能可以将其推广到不同的医学领域的其他AE提取任务中.

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