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

支持协作以提高 AI/ML 准备度的行政补充

• 批准号: 10412233
• 负责人: Olga F. Jarrín Montaner
• 金额: $ 14.87万
• 依托单位: RUTGERS BIOMEDICAL/HEALTH SCIENCES-RBHS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10412233
• 关键词: Administrative Supplement; Adopted; Advanced Development; Aging; Architecture; Award; Caring; Clinical; Code; Collaborations; Complex; Custom; Data; Data Science; Data Set; Development; Distal; Documentation; Economics; Elderly; Engineering; Ensure; Environmental Risk Factor; Exclusion Criteria; FAIR principles; Goals; Health; Individual; Intercept; Life; Maps; Measures; Medicare; Methodology; Mission; Modeling; Modernization; National Institute on Aging; Outcome; Parents; Patients; Pattern; Physical Function; Practice Guidelines; Procedures; Public Health; Readiness; Reproducibility; Research; Research Personnel; Resources; Risk Factors; Source Code; Standardization; Syndrome; Taxonomy; Technology; Time; United States National Institutes of Health; Universities; Vision; Work; aging population; analytical method; base; beneficiary; cohort; data centers; data cleaning; data ecosystem; data enclave; data harmonization; data infrastructure; data warehouse; experience; feature extraction; health disparity; improved; inclusion criteria; machine learning algorithm; minority health; multiple datasets; open source; repository; risk sharing; social; socioeconomics; virtual

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.

总结 本申请是对现有裁决的行政补充（修订），R33 AG 068931，“高级 多数据集组合老化轨迹文件的开发与利用。“目标的 母研究是创建一个全面的研究库的老化轨迹数据集，并证明 他们通过4个具体目标在罗格斯大学老龄化研究的效用：1）协调和合并 多个数据集，以生成理解护理环境中随时间的变化所需的数据基础设施， 老年综合征、身体功能和多个水平和多个领域的共同风险因素 2）开发最先进的分析方法，以确定经历的老化轨迹模式 老年人在生命的最后几年及其与共同风险因素和远端结局的关系， 3)使用基于模型和基于模型的方法发现轨迹的多层次和潜在交互式预测因子 方法和机器学习算法来预测特定的结果，以及4）传播资源 包括数据集、文档、源代码和方法。 对于补充，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。数据统一 需要针对每个数据仓库的服务器体系结构和资源定制过程， 需要VRDC特定的工作流程和代码，以确保及时访问和再现性。在这个项目中， 数据在四个阶段进行AI/ML准备：1）定义要研究的患者队列， 和排除标准变量; 2）数据预处理步骤，包括数据清洗、数据 注释、格式化、标准化分类、变量转换、数据重新缩放/规范化、变量 汇总、变量分解和变量选择，重点是对衡量重要的变量 健康差距和改善少数民族健康和减少健康差距; 3）特征提取和 工程包括生成导出变量（例如，截距、斜率、平均值等）从不规则间隔的 个人轨迹;以及4）医疗保险数据集与公开数据合并，以增加社会经济 和环境上下文，以及数据变量关系被映射以产生最终的AI/ML就绪数据。 补充目标。开发和实施数据预处理、数据微调和精度、缺失的代码 数据插补，数据连接和完全建立的AI/ML框架的层次关系， 交互式地模拟老年衰老轨迹和医疗保险受益人队列中的选定结果。 这项工作的完成将有助于NIH实现现代化和综合生物医学数据的愿景 采用最新数据科学技术和最佳实践指南（包括FAIR）的生态系统 （可查找、可访问、可互操作、可重用）原则和开源开发。