Containerizing tasks to ensure robust AI/ML data curation pipelines to estimate environmental disparities in the rural south

项目摘要 我们的父R 01解决了一个主要的科学差距，研究农村人口的健康，包括农村人口的健康， 种族和少数民族群体受到空气污染和极端气温的影响。作为父母的一部分 R 01我们1）生成空气污染，热，冷，健康，社会经济地位（SES）的数据架构， 城市和农村形式，以及弗吉尼亚州和西弗吉尼亚州的其他因素; 2）估计暴露的差异 按种族/民族、社会经济地位和农村划分的空气污染和天气（寒冷、炎热和热浪）， 农村形式的差异; 3）估计暴露于空气污染之间的关联差异 或天气和健康的非常年轻（不利的出生结果）和老年人（住院 65岁以上的人），考虑到各种脆弱性因素（社会经济地位低、种族/族裔）的差异， 城市/农村形式。 作为R 01的一部分，我们在生成空气污染数据架构方面取得了重大进展， 热、冷、健康、社会经济地位、城市/农村形式和其他因素。更具体地说，我们获得了健康数据， 医疗保险和医疗补助服务中心（CMS）。然而，虽然取得了相当大的进展， 在开发CMS索赔数据的数据处理管道时，CMS数据的隐私和保密性 限制阻碍了预处理数据集的共享，导致数据清理和处理的重复 努力这种重复的努力可能是浪费的，因为研究人员可能无法建立在彼此的工作基础上 或有效合作。虽然数据无法共享，但共享开放的处理管道对于 消除重复工作，并允许更容易获得AI/ML就绪数据。共享工作流时 容器对于确保再现性至关重要。 通过这种管理补充，我们的目标是采用容器化的数据处理任务， 加强父R 01和更广泛研究中CMS数据的AI/ML管道部署 社区.容器的使用使得能够容易地交换或更新 处理管道，可以在不同研究人员共享的AI/ML管道中重复使用/回收， 研究团队和更广泛的研究机构。数据处理采用集装箱化 任务增强了我们的亲本R 01的再现性，并允许优化计算资源 高性能计算（HPC）。 基于容器的AI/ML管道将加快父R 01的研究速度，使我们能够 严格估计暴露于空气污染和天气对健康的影响之间的差异 结果。此外，这些改进对于传播工作流程至关重要 管道在更广泛的研究社区。

Project Summary Our parent R01 addresses a major scientific gap studying how the health of rural populations, including rural racial and ethnic minority groups, is impacted by air pollution and extreme temperature. As part of the parent R01 we are 1) generating a data architecture for air pollution, heat, cold, health, socioeconomic status (SES), urban and rural form, and other factors in Virginia and West Virginia; 2) estimating the disparities in exposure to air pollution and weather (cold, heat, and heat waves) by race/ethnicity, SES, and rurality, accounting for variations in rural form; and 3) estimating the disparities in the associations between exposure to air pollution or weather and health for the very young (adverse birth outcomes) and older populations (hospital admissions for those >65 years), considering differences by various vulnerability factors (low SES, race/ethnicity) and urban/rural form. As part of the parent R01, we have made significant progress in generating a data architecture for air pollution, heat, cold, health, SES, urban/rural form, and other factors. More specifically we have obtained health data from the Centers of Medicare and Medicaid services (CMS). However, while considerable progress has been made in developing data processing pipelines for CMS claims data, the CMS data privacy and confidentiality limitations hinder the sharing of preprocessed datasets, leading to duplication of data cleaning and processing efforts. This duplication of effort can be wasteful, as researchers may not be able to build on each other's work or collaborate effectively. While data cannot be shared, sharing of open processing pipelines is crucial to eliminate duplication efforts and allow for more readily available AI/ML ready data. When workflows are shared containers are critical to ensure reproducibility. With this administrative supplement, our goal is the adoption of containerized data processing tasks to enhance the deployment of AI/ML pipelines for CMS data in the parent R01 and the wider research communities. The use of containers enables the easy exchange or updating of single components in a processing pipeline, which can be reused/recycled across AI/ML pipelines shared by different investigators in the study team and more broadly across research institutions. The adoption of data processing containerized tasks enhances reproducibility of our parent R01 and allows for the optimization of computational resources over High-Performance Computing (HPC). The container-based AI/ML pipelines will accelerate the velocity of research in the parent R01 allowing us to rigorously estimate the disparities in the associations between exposure to air pollution and weather on health outcomes. Furthermore, these improvements are crucial to allow for the dissemination of the workflow pipelines across the wider research community.