使用自然语言处理识别新出现的公共卫生事件的早期信号
Kelly S Peterson1,2,3, Christian Dalton2,3, Andrea Kalvesmaki3
1Veterans Health Administration, Office of Analytics and Performance Integration, Washington, District of Columbia, USA.
Interdisciplinary perspectives on infectious diseases
|March 9, 2026
概括
通过识别非特异性信号,如当局通信和病原体暴露的提及,提前检测公共卫生威胁得到了改善. 这项研究证明了使用自动化方法进行大规模生物监测的可行性.
科学领域:
- 公共卫生 公共卫生
- 传染病监测 传染病监测 传染病
- 计算流行病学计算流行病学
背景情况:
- 新出现的公共卫生威胁带来了挑战,因为监控系统没有针对新事件进行优化.
- 在各种新兴威胁中识别共同的早期信号对于及时检测至关重要.
研究的目的:
- 识别和评估新出现的公共卫生威胁的非特定早期信号.
- 为了评估公共卫生当局沟通的有用性,动物感染暴露和其他病原体暴露被提及为早期指标.
主要方法:
- 使用了美国退伍军人事务部紧急部门访问数据 (2004-2024年).
- 采用基于规则和转换器模型进行自然语言处理,从超过3300万份临床文档中提取目标概念.
- 基于历史诊断和实验室数据为选定的传染病构建的培训和验证集.
主要成果:
- 提取的信号的积极预测值在0.615到1.0.0之间.
- 提取的暴露分布与已识别的病原体的预期保持一致.
- 从历史临床文件中成功提取相关病例.
结论:
- 自动化自然语言处理能够对临床文件进行可扩展的监控,以识别相关病例.
- 最初的验证表明生物监测中可行性探索的准确性是可以接受的.
- 非特定的早期信号显示出增强对新出现的公共卫生威胁的检测的希望.
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