在电子健康记录中启用人工智能诊断预测,以增强生物监控和早期疫情检测
Andre Goncalves1, Jose Cadena1, Yeping Hu1
1Lawrence Livermore National Laboratory.
Research square
|June 30, 2025
概括
这项研究通过将机器学习 (ML) 预测与传统监测相结合,提高了传染病爆发的检测能力. 综合方法使得疫情的早期识别成为可能,改善了公共卫生反应.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 机器学习 机器学习
背景情况:
- 迅速发现传染病爆发对公共卫生至关重要.
- 传统的流行病学监测在及时识别疫情爆发方面面临挑战.
- 集成先进的计算方法可以提高生物监控能力.
研究的目的:
- 开发和评估一种新的生物监测方法,将机器学习的诊断预测与流行病监测相结合.
- 提高传染病爆发检测的速度和准确性.
- 评估综合方法的交付时间和假阳性率.
主要方法:
- 从2010年到2022年利用450万名患者记录进行模型培训.
- 开发机器学习模型,在24小时间隔内预测传染病可能性.
- 集成的高可信度ML预测与空间时间爆发分析的最终诊断.
主要成果:
- 早期检测到33.3%的相关传染病爆发 (1-24天的带头时间).
- 平均每年检测到1.33个虚假阳性疫情.
- 证明了ML集成的可行性,以实现更快的生物监测.
结论:
- 将机器学习与传统的流行病学监测相结合,显著提高了疫情早期检测.
- 这种混合方法为改善公共卫生反应时间提供了一个有希望的策略.
- 这种方法的有效性取决于有足够的疾病特异性培训数据.
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