BD Spokes: SPOKE: NORTHEAST: Collaborative Research: Integration of Environmental Factors and Causal Reasoning Approaches for Large-Scale Observational Health Research

如今，海量的健康、环境和行为数据正在产生，但它们仍然被锁定在数字孤岛中。例如，来自医院等医疗保健提供者的数据提供了个人和人群从出生到死亡的健康状况的动态视图。与此同时，政府机构和行业向公众发布了大量的经济、环境和行为数据，如收入/贫困、不利暴露(如空气污染)和生态因素(如气候)的指标。经济、环境和行为因素与健康有何联系？该项目将汇集大量环境和临床数据流的来源，使科学界能够解决这一问题。通过打破当前的数据孤岛，更广泛的科学影响将是广泛的。首先，这一努力将为大数据社区培育生物医学调查的新途径。其次，该项目将实现与行为、经济、环境和公共健康相关的发现。该项目的目标是组装一个包含大量健康/临床、环境、行为和经济数据流的首个数据仓库，以最终实现这些数据源之间的因果发现。首先，该团队将通过利用观察性健康数据科学和信息学(OHDSI，www.ohdsi.org)网络整合大量健康数据流，该网络是一个虚拟数据存储库，包含数百万纵向患者测量数据，如药物和疾病诊断。其次，该团队将建立一个中央数据仓库，其中包含全美重要的环境、行为和经济数据，如环境保护局空气污染空气数据、美国人口普查收入和职业统计数据，以及国家海洋管理局气候和天气相关信息协会。第三，该团队将传播用于因果推断和机器学习的新兴计算方法，使研究人员能够找到环境、经济、行为和临床因素之间的因果联系。该团队将利用我们由学术大数据研究人员、联邦级研究所(例如EPA、NOAA)和医院(例如合作伙伴医疗保健)组成的广泛协作网络来集成这些数据并传播尖端的机器学习工具。最后，该项目将创建培训资源(例如，交互式操作指南)，协调跨机构的学生实习，并领导一个实践讲习班，以演示综合数据仓库的使用。该项目的最终目标是通过传播集成和开放的大数据和分析工具，促进社区主导和协作的因果关系发现。

Vast quantities of health, environmental, and behavioral data are being generated today, yet they remain locked in digital silos. For example, data from health care providers, such as hospitals, provide a dynamic view of health of individuals and populations from birth to death. At the same time, government institutions and industry have released troves of economic, environmental, and behavioral datasets, such as indicators of income/poverty, adverse exposure (e.g., air pollution), and ecological factors (e.g., climate) to the public domain. How are economic, environmental, and behavioral factors linked with health? This project will put together numerous sources of large environmental and clinical data streams to enable the scientific community to address this question. By breaking current data silos, the broader scientific impacts will be wide. First, this effort will foster new routes of biomedical investigation for the big data community. Second, the project will enable discoveries that will have behavioral, economic, environmental, and public health relevance.This project will aim to assemble a first-ever data warehouse containing numerous health/clinical, environmental, behavioral, and economic data streams to ultimately enable causal discovery between these data sources. First, the team will integrate numerous health data streams by leveraging the Observational Health Data Sciences and Informatics (OHDSI, www.ohdsi.org) network, a virtual data repository that contains millions of longitudinal patient measurements, such as drugs and disease diagnoses. Second, the team will build a centralized data warehouse that contains important environmental, behavioral, and economic data across the United States, such as the Environmental Protection Agency air pollution AirData, the United States Census data on income and occupation statistics, and the National Oceanic Administration Association for climate and weather-related information. Third, the team will disseminate emerging computational methods for causal inference and machine learning to enable researchers to find causal links between environmental, economic, behavioral, and clinical factors. The team will leverage our broad collaborative network consisting of academic big data researchers, federal-level institutes (e.g., EPA, NOAA), and hospitals (e.g., Partners HealthCare) to integrate these data and to disseminate cutting edge machine learning tools. Lastly, the project will create training resources (e.g., interactive how-to guides), coordinate cross-institution student internships, and lead a hands-on workshop to demonstrate use of the integrated data warehouse. The ultimate goal of the project is to facilitate community-led and collaborative causal discovery through dissemination of integrated and open big data and analytics tools.