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

BD 发言：发言：东北：合作研究：大规模观察健康研究的环境因素和因果推理方法的整合

• 批准号: 1636795
• 负责人: Vasant Honavar
• 金额: $ 9.54万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636795&HistoricalAwards=false
• 关键词: BD; Spokes; SPOKE; NORTHEAST; Collaborative

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.

今天，大量的健康、环境和行为数据正在生成，但它们仍然被锁定在数字孤岛中。例如，来自医疗保健提供者（如医院）的数据提供了从出生到死亡的个人和群体健康的动态视图。与此同时，政府机构和行业已经发布了大量的经济、环境和行为数据集，如收入/贫困指标、不利风险（例如，空气污染），和生态因素（例如，气候）到公共领域。经济、环境和行为因素如何与健康相关？该项目将汇集大量的环境和临床数据流，使科学界能够解决这个问题。通过打破目前的数据孤岛，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新的生物医学研究路线。第二，该项目将实现与行为、经济、环境和公共卫生相关的发现。该项目的目标是组装一个首个包含大量健康/临床、环境、行为和经济数据流的数据仓库，最终实现这些数据源之间的因果发现。首先，该团队将利用观察性健康数据科学和信息学（OHDSI，www.ohdsi.org）网络整合众多健康数据流，该网络是一个虚拟数据存储库，包含数百万个纵向患者测量值，如药物和疾病诊断。其次，该团队将建立一个集中的数据仓库，其中包含美国各地重要的环境，行为和经济数据，例如环境保护局的空气污染AirData，美国人口普查的收入和职业统计数据，以及国家海洋局协会的气候和天气相关信息。第三，该团队将传播用于因果推理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关系。该团队将利用我们广泛的合作网络，包括学术大数据研究人员，联邦级机构（例如，EPA、NOAA）和医院（例如，Partners HealthCare）整合这些数据，并传播最先进的机器学习工具。最后，该项目将创建培训资源（例如，交互式操作指南），协调跨机构学生实习，并领导一个实践讲习班，以展示综合数据仓库的使用。该项目的最终目标是通过传播综合和开放的大数据和分析工具，促进社区主导和协作的因果发现。

项目成果

Towards Conditional Independence Test for Relational Data.

关系数据的条件独立性测试。

• 发表时间: 2017
• 期刊: Conference on Uncertainty in Artificial Intelligence
• 作者: Sanghack Lee;Vasant G Honavar
• 通讯作者: Vasant G Honavar

Functional Autoencoders for Functional Data Representation Learning

• DOI: 10.1137/1.9781611976700.75
• 发表时间: 2021-01
• 作者: Tsung-Yu Hsieh;Yiwei Sun;Suhang Wang;Vasant G Honavar

SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series

SrVARM：状态正则化向量自回归模型，用于联合学习多变量时间序列中的隐藏状态转换和状态相关变量间依赖性

• DOI: 10.1145/3442381.3450116
• 发表时间: 2021
• 期刊: WWW '21: Proceedings of the Web Conference 2021
• 作者: Hsieh, Tsung-Yu;Sun, Yiwei;Tang, Xianfeng;Wang, Suhang;Honavar, Vasant G.
• 通讯作者: Honavar, Vasant G.

Self-Discrepancy Conditional Independence Test

自差异条件独立性检验

• 发表时间: 2017
• 期刊: Uncertainty in artificial intelligence
• 作者: Lee, Sanghack;Honavar, Vasant G
• 通讯作者: Honavar, Vasant G

Multi-view Network Embedding via Graph Factorization Clustering and Co-regularized Multi-view Agreement

• DOI: 10.1109/icdmw.2018.00145
• 发表时间: 2018-11
• 期刊: 2018 IEEE International Conference on Data Mining Workshops (ICDMW)
• 作者: Yiwei Sun;N. Bui;Tsung-Yu Hsieh;Vasant G Honavar