使用大型语言模型从公开来源自动提取死亡率信息:开发和评估研究研究
Mohammed Al-Garadi1, Michele LeNoue-Newton1, Michael E Matheny1
1Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN, 37203, United States, 1 2139151696.
Journal of medical Internet research
|August 18, 2025
概括
新的NLP和LLM方法从在线来源提取死亡数据,改善监控. 这些工具提高了公共卫生和医疗产品安全的及时性和完整性.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 公共卫生监督 公共卫生监督
背景情况:
- 传统的死亡数据来源 (如国家死亡指数,EHR) 面临着数据滞后,信息缺失和不完全覆盖的挑战.
- 来自社交媒体,众筹和纪念馆的公开数字内容为死亡率监测提供了潜在的补充数据来源.
- 从非结构化在线数据中提取死亡率信息的现有工具尚未得到充分开发.
研究的目的:
- 开发可扩展的自然语言处理 (NLP) 和大型语言模型 (LLM) 方法,从各种基于网络的数据源中提取死亡率信息.
- 评估这些NLP和LLM方法在各种在线平台上的表现,包括社交媒体,众筹网站和基于Web的告.
主要方法:
- 收集了来自X (以前的Twitter),GoFundMe,EverLoved,TributeArchive和网络告 (2015-2022) 的美国相关死亡数据.
- 开发了一个基于变压器的NLP管道来提取死者的姓名,出生日期和死亡日期.
- 采用几次射击学习 (FSL) 方法与LLM识别主要和次要死亡原因 (CoD),与人类注释或裁决数据对性能进行评估.
主要成果:
- 性能最好的模型在死亡率信息提取方面获得了0.88的微平均F1得分.
- 该FSL-LLM方法在识别初级Cod方面表现出很高的准确性,在GoFundMe上达到95.9%,在告上达到96.5%,在纪念网站上达到98%.
- 在不同数据源中,模型的性能接近于人类注释者/评判者的表现.
结论:
- 先进的NLP和LLM技术可用于从公共网络源中提取死亡率数据,提高监测的及时性和完整性.
- 这些数字数据源为传统系统提供了有价值的补充,有可能改善公共卫生监测和医疗产品安全评估.
- 建议进一步验证并纳入国家监测系统,以在现实世界医疗保健环境中利用这些发现.
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