Administrative Supplement to Support Collaborations to Improve AI/ML-Readiness

摘要 本申请是对现有奖励R33AG068931的行政补充(修订)。 从多个数据集中组合的老化轨迹文件的开发和利用。 家长研究是创建老化轨迹数据集的全面研究存储库，并演示 它们通过四个具体目标对罗格斯大学的老龄化研究产生作用：1)协调和融合 多个数据集以生成理解护理环境中随时间变化所需的数据基础设施， 多层次、跨多个层次的老年综合征、身体功能和共同风险因素 领域，2)开发最先进的分析方法，以确定所经历的老化轨迹的模式 老年人在生命的最后几年及其与共同风险因素和远期结局的关联， 3)使用基于模型的方法发现轨迹的多层次和潜在的交互预测器 预测特定结果的方法和机器学习算法，以及4)传播资源 生成的数据包括数据集、文档、源代码和方法。 作为补充，CMS虚拟研究数据中心(VRDC)的新工作将创建AI/ML就绪 用于数据清理和预处理的数据集、工作流和源代码，打破了孤立的障碍 在VRDC工作的研究人员和大学的机构数据飞地之间。数据协调 程序需要针对每个数据仓库的服务器架构和资源进行定制， 需要特定于VRDC的工作流程和代码，以确保及时访问和重现性。在这个项目中， 数据分四个阶段为AI/ML做好准备：1)确定要研究的患者队列，并关键纳入 2)数据前处理步骤包括数据清理、数据 注解、格式化、标准化分类、变量转换、数据重定标/标准化、变量 聚合、变量分解和变量选择，重点关注重要的测量变量 健康差距和改善少数群体健康和缩小健康差距；3)特征提取和 工程包括生成派生变量(例如，截距、斜率、平均值等)。从不规则间隔开始 个人轨迹；以及4)将医疗保险数据集与公共可用数据合并，以添加社会经济 和环境背景，以及数据变量关系被映射以产生最终的、AI/ML就绪的数据。 补充目的。开发并实现数据前处理、数据微调和精度缺失的代码 AI/ML框架的数据归责、数据连接和完全建立的层次关系 在一组医疗保险受益者队列中，交互式地模拟晚年老龄化轨迹和选定的结果。 这项工作的完成将有助于NIH实现现代化和集成的生物医学数据的愿景 采用最新数据科学技术的生态系统，以及包括FAIR在内的最佳实践指南 (可查找、可访问、可互操作、可重复使用)原则和开源开发。

SUMMARY This application is for an administrative supplement (revision) to an existing award, R33AG068931, "Advanced Development and Utilization of Assembled Aging Trajectory Files from Multiple Datasets." The goal of the parent study is to create a comprehensive research repository of aging trajectory datasets and to demonstrate their utility for aging research at Rutgers University through 4 specific aims: 1) Harmonizing and merging multiple data sets to generate the data infrastructure needed to understand change over time in care settings, geriatric syndromes, physical functioning, and shared risk factors at multiple levels and across multiple domains, 2) Developing state-of-the-art analytic methods to identify patterns of aging trajectories experienced by older adults during the final years of life and their association with shared risk factors and distal outcomes, 3) Discovering multilevel and potentially interactive predictors of trajectories using both model-based approaches and machine learning algorithms to predict specific outcomes, and 4) Disseminating resources generated including datasets, documentation, source code, and methodology. For the supplement, new work in the CMS Virtual Research Data Center (VRDC) will create AI/ML-ready datasets, workflows, and source code for data cleaning and pre-processing, breaking the siloed barriers between researchers working in the VRDC and institutional data enclaves at Universities. Data harmonization procedures need to be customized to the server architecture and resources of each data warehouse, necessitating VRDC-specific workflows and code to ensure timely access and reproducibility. In this project, data are made AI/ML-ready in four stages: 1) the cohort of patients to be studied is defined and key inclusion and exclusion criteria variables are selected; 2) data pre-processing steps include data cleaning, data annotation, formatting, standardizing taxonomies, variables transformation, data rescale/normalization, variable aggregating, variable decomposing and variable selection with a focus on variables important to measure health disparities and improve minority health and reduce health disparities; 3) feature extraction and engineering include generating derived variables (e.g., intercept, slope, average, etc.) from irregularly spaced individual trajectories; and 4) Medicare data sets are merged with publicly available data to add socioeconomic and environmental context, and data variable relationships are mapped to produce a final, AI/ML-ready data. Supplement Aim. Develop and implement code for data pre-processing, data fine-tuning and precision, missing data imputation, data connectivity and fully established hierarchical relationships for the AI/ML framework to interactively model late-life aging trajectories and selected outcomes in a cohort of Medicare beneficiaries. Completion of this work will contribute to the NIH vision of a modernized and integrated biomedical data ecosystem that adopts the latest data science technologies, and best practice guidelines including FAIR (findable, accessible, interoperable, reusable) principles and open-source development.