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

项目摘要 我们的母公司R01解决了一个重大的科学空白，研究如何提高包括农村人口在内的农村人口的健康 少数族裔群体受到空气污染和极端气温的影响。作为父级的一部分 R01我们正在为空气污染、高温、寒冷、健康、社会经济状况(SES)生成数据架构， 城市和农村形态，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 到空气污染和天气(冷、热和热浪)，按种族/民族、SES和乡村划分，占 农村形式的变化；以及3)估计暴露于空气污染之间的差异 对于非常年轻的人(不利的出生结果)和老年人口(住院)的天气和健康 对于那些65岁的人)，考虑到不同脆弱性因素的差异(SES低、种族/族裔)和 城市/乡村形式。 作为母公司R01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， 高温、寒冷、健康、社会经济状况、城市/农村形态等因素。更具体地说，我们已经获得了健康数据 来自医疗保险和医疗补助服务中心(CMS)。然而，尽管取得了相当大的进展， 提出了在开发CMS索赔数据处理流水线时，CMS数据的隐私性和保密性 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。这种重复的工作可能是浪费的，因为研究人员可能无法在彼此的工作基础上进行改进 或有效协作。虽然数据不能共享，但共享开放的处理管道对 消除重复工作，并支持更容易获得的AI/ML就绪数据。共享工作流时 容器是确保重现性的关键。 通过此管理补充，我们的目标是采用集装箱化的数据处理任务来 加强母公司R01和更广泛研究中CMS数据的AI/ML管道部署 社区。通过使用容器，可以轻松地交换或更新 处理流水线，可以在不同调查人员共享的AI/ML流水线之间重复使用/回收 研究团队和更广泛的研究机构。数据处理采用集装化 任务增强了父R01的重现性，并允许优化计算资源 超高性能计算(HPC)。 基于容器的AI/ML管道将加快父R01的研究速度，使我们能够 严格评估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，这些改进对于工作流程的传播至关重要 横跨更广泛的研究社区的管道。

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.