In 2016, Centers for Disease Control and Prevention (CDC) established surveillance of pregnant women with Zika virus infection and their infants in the U.S. states, territories, and freely associated states. To identify cases of Zika‐associated birth defects, subject matter experts review data reported from medical records of completed pregnancies to identify findings that meet surveillance case criteria (manual review). The volume of reported data increased over the course of the Zika virus outbreak in the Americas, challenging the resources of the surveillance system to conduct manual review. Machine learning was explored as a possible method for predicting case status. Ensemble models (using machine learning algorithms including support vector machines, logistic regression, random forests, k‐nearest neighbors, gradient boosted trees, and decision trees) were developed and trained using data collected from January 2016–October 2017. Models were developed separately, on data from the U.S. states, non‐Puerto Rico territories, and freely associated states (referred to as the U.S. Zika Pregnancy and Infant Registry [USZPIR]) and data from Puerto Rico (referred to as the Zika Active Pregnancy Surveillance System [ZAPSS]) due to differences in data collection and storage methods. The machine learning models demonstrated high sensitivity for identifying cases while potentially reducing volume of data for manual review (USZPIR: 96% sensitivity, 25% reduction in review volume; ZAPSS: 97% sensitivity, 50% reduction in review volume). Machine learning models show potential for identifying cases of Zika‐associated birth defects and for reducing volume of data for manual review, a potential benefit in other public health emergency response settings.

中文翻译：

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用于监测寨卡相关出生缺陷的机器学习方法的探索性分析。

2016 年，疾病控制和预防中心 (CDC) 在美国各州、领地和自由关联州建立了对感染寨卡病毒的孕妇及其婴儿的监测。为了识别与寨卡病毒相关的出生缺陷病例，主题专家审查从已完成妊娠的医疗记录中报告的数据，以确定符合监测病例标准的结果（人工审查）。在美洲寨卡病毒爆发期间，报告的数据量有所增加，这对监测系统进行人工审查的资源提出了挑战。机器学习被探索为预测病例状态的一种可能方法。集成模型（使用机器学习算法，包括支持向量机、逻辑回归、随机森林、k近邻、梯度提升树和决策树）是使用 2016 年 1 月至 2017 年 10 月收集的数据开发和训练的。模型是根据来自美国各州、非波多黎各领土和自由联合州（参考由于数据收集和存储方法的差异，美国寨卡妊娠和婴儿登记处 [USZPIR]）和波多黎各的数据（称为寨卡主动妊娠监测系统 [ZAPSS]）。机器学习模型在识别案例方面表现出很高的敏感性，同时可能减少人工审查的数据量（USZPIR：96% 的敏感性，审查量减少 25%；ZAPSS：97% 的敏感性，审查量减少 50%）。