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人工智能增强了医疗保健疫情调查中的基因组监测
Alexander Sundermann1,2,3, Jieshi Chen4, Melissa Saul5
1Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburghhttps://ror.org/01an3r305, Pittsburgh, PA, USA.
Infection control and hospital epidemiology
|November 14, 2025
概括
人工智能 (AI) 通过自动化传播路线识别来加强医疗保健疫情调查. 与手动图表审查相比,这种AI工具显著提高了检测准确度和效率.
科学领域:
- 医疗保健流行病学 医疗保健流行病学
- 传染病监测 传染病监测 传染病监测 传染病监测
- 医学中的人工智能.
背景情况:
- 疫情调查和控制对于防止传染病在医疗机构传播至关重要.
- 识别传输路线的传统手动方法耗时且有限.
- 全基因组测序 (WGS) 改善了疫情的检测,但仍然需要广泛的手动图表审查.
研究的目的:
- 评估一款旨在简化医疗保健疫情调查的人工智能 (AI) 工具.
- 评估人工智能工具在识别传输路线方面的表现,与手动专家审查相比.
主要方法:
- 评估了与医疗相关传播 (EDS-HAT) 增强检测系统 (EDS-HAT) 集成的AI工具.
- 分析了2021年11月至2023年11月期间爆发的患者电子健康记录 (EHR) 的数据.
- 人工智能算法的识别传输路线的能力与手动图表审查进行了比较.
主要成果:
- 人工智能工具确定了手动审查中遗漏的37条传输路径,包括程序和提供商相关的路径.
- 与手动审查相比,该算法显示了76.0%的灵敏度.
- 当计算设施间传输和下游风险时,敏感性增加到91.7%.
结论:
- 通过自动化传播路线识别,EDS-HAT人工智能工具显著增强了疫情调查.
- 人工智能工具成功地确定了传统图表审查方法错过的额外传输路径.
- 将人工智能与基因组监测相结合,有可能优化疫情检测和医疗干预.
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