Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) - Admin Supplement

1个项目摘要 有证据表明，接触超级基金化学品会导致不良妊娠结局，包括 早产(PTB)。波多黎各的肺结核和婴儿死亡率是美国各州中最高的， 领地。在PR有18个超级基金站点，饮用水受到污染的证据很多。此外， 极端天气事件(飓风、flOding)可能会导致超级基金化学品暴露增加。被保护者 该中心汇集了来自东北大学、波多黎各大学、佐治亚大学、 和密歇根大学就这一关系和机制提供了亟需的理解 暴露于可疑化学品有助于APO的发生，并开发新的方法来降低公关及其他领域的暴露风险。 为了做到这一点，保护使用了源到结果的结构，将流行病学、毒理学、命运和运输结合在一起， 和补救研究、统一的抽样基础设施、集中的索引数据存储库和复杂的数据 管理系统。 自2010年成立以来，Protect已经建立了关于环境条件和产前的详细和广泛的数据集 孕妇的情况(接触情况、社会经济和健康数据--接近每个产妇2400多个数据点-- IPant)，从2000多名孕妇和她们的孩子的队列中收集了丰富的数据集。被保护者 数据管理和分析核心(DMAC)集中管理整个中心的数据，同时提供两种分析 支持对定量和定性数据进行评估。DMAC为此提供了一套全面的数据字典 数据集，并开发了适当处理敏感数据的协议。 Protecte数据库有可能帮助解锁可能将环境因素与早产联系在一起的关系 出生结局。该数据集已准备好利用强大的AI/ML工具集来帮助识别和建立这些关系。 但在我们开始利用这些强大的工具之前，必须克服特殊的fic挑战来准备数据。这些 挑战包括数据集中缺失数据的程度，以及数据中固有的类不平衡。这个项目将 正面解决这些问题，并利用通过举办一系列黑客松来获得的反馈和经验 这些数据集。其结果应该是产生一套准备用于AI/ML工具的数据集，并交付许多 新的开源工具集，用于解决数据缺失和不平衡问题。

1 Project Summary Evidence suggests that exposure to Superfund chemicals contributes to adverse pregnancy outcomes (APOs), including preterm birth (PTB). Rates of PTB and infant mortality in Puerto Rico (PR) are among the highest of all US states and territories. There are 18 Superfund sites in PR, and evidence of contamination of the drinking water is extensive. Moreover, extreme weather events (hurricanes, flooding) may result in elevated exposures to Superfund chemicals. The PROTECT center has brought together researchers from Northeastern University, the University of Puerto Rico, University of Georgia, and the University of Michigan to provide much needed understanding of the relationship and the mechanisms by which exposure to suspect chemicals contribute to APOs, and to develop new methods to reduce risk of exposure in PR and beyond. To do this, PROTECT uses a source-to-outcome structure, integrating epidemiological, toxicological, fate and transport, and remediation studies, a unified sampling infrastructure, a centralized indexed data repository, and a sophisticated data management system. Since its inception in 2010, PROTECT has built detailed and extensive datasets on environmental conditions and prenatal conditions of pregnant mothers (exposure, socioeconomic and health data–close to more than 2400 data points per partic- ipant), yielding a rich dataset collected from a cohort of over 2000 expectant mothers and their children. The PROTECT Data Management and Analytics Core (DMAC) manages data centrally for the entire Center, while providing both analytics support to evaluate quantitative and qualitative data. The DMAC provides a comprehensive set of Data Dictionaries for this dataset and developed protocols for proper handling of sensitive data. The PROTECT database has the potential to help unlock the relationships that can tie environmental factors to preterm birth outcomes. The dataset is primed to leverage powerful AI/ML toolsets to help identify and establish these relationships. But before we can start leveraging these powerful tools, specific challenges must be overcome to prepare the data. These challenges include the degree of missing data in our dataset, and the inherent class imbalance in our data. This project will address these issues head on, and utilize feedback and experience developed through hold a series of hackathons that explore these datasets. The result should be the production of a suite of datasets ready for AI/ML tools, and the delivery a number of new open source toolsets to addressing missingness and imbalance in data.