用AI排名法提升公共卫生数据监测效率，提速54倍。
An AI-Based Public Health Data Monitoring System
- 改用AI异常检测的排名式监控，避免阈值频繁调整。
- 日处理500万条数据，评审效率提升54倍。
- 适合疾控中心、卫生部门等需要实时数据监控的机构。
公共卫生专家需要可扩展的方法来监控大规模健康数据（如病例、住院、死亡数），以发现疫情或数据质量问题。传统基于告警的监测系统因告警阈值需不断重设，且数据量大导致应用延迟而面临挑战。为此，我们提出一种基于AI异常检测的排名式监测范式。通过多年跨学科合作，该系统已在国家级机构部署，每日监控高达500万条数据。为期三个月的纵向评估显示，监测目标实现显著提升，评审速度效率相比传统告警方法提高54倍。本研究展示了以人为本的AI在变革公共卫生决策中的潜力。
原文摘要 · Abstract (English)
Public health experts need scalable approaches to monitor large volumes of health data (e.g., cases, hospitalizations, deaths) for outbreaks or data quality issues. Traditional alert-based monitoring systems struggle with modern public health data monitoring systems for several reasons, including that alerting thresholds need to be constantly reset and the data volumes may cause application lag. Instead, we propose a ranking-based monitoring paradigm that leverages new AI anomaly detection methods. Through a multi-year interdisciplinary collaboration, the resulting system has been deployed at a national organization to monitor up to 5,000,000 data points daily. A three-month longitudinal deployed evaluation revealed a significant improvement in monitoring objectives, with a 54x increase in reviewer speed efficiency compared to traditional alert-based methods. This work highlights the potential of human-centered AI to transform public health decision-making.
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