Nobles Mallory, Lall Ramona, Mathes Robert W, Neill Daniel B
H.J. Heinz III College, Carnegie Mellon University, Pittsburgh, PA, USA.
New York City Department of Health and Mental Hygiene, New York, NY, USA.
Sci Adv. 2022 Nov 4;8(44):eabm4920. doi: 10.1126/sciadv.abm4920.
Existing public health surveillance systems that rely on predefined symptom categories, or syndromes, are effective at monitoring known illnesses, but there is a critical need for innovation in "presyndromic" surveillance that detects biothreats with rare or previously unseen symptomology. We introduce a data-driven, automated machine learning approach for presyndromic surveillance that learns newly emerging syndromes from free-text emergency department chief complaints, identifies localized case clusters among subpopulations, and incorporates practitioner feedback to automatically distinguish between relevant and irrelevant clusters, thus providing personalized, actionable decision support. Blinded evaluations by New York City's Department of Health and Mental Hygiene demonstrate that our approach identifies more events of public health interest and achieves a lower false-positive rate compared to a state-of-the-art baseline.
现有的依赖预定义症状类别或综合征的公共卫生监测系统在监测已知疾病方面很有效，但对于“综合征前”监测的创新有迫切需求，这种监测能检测出具有罕见或前所未见症状的生物威胁。我们引入一种用于综合征前监测的数据驱动型自动化机器学习方法，该方法从急诊科自由文本形式的主诉中学习新出现的综合征，识别亚人群中的局部病例集群，并纳入从业者反馈以自动区分相关和不相关的集群，从而提供个性化的、可采取行动的决策支持。纽约市卫生和精神卫生部门的盲法评估表明，与最先进的基线方法相比，我们的方法能识别出更多具有公共卫生意义的事件，且假阳性率更低。
