A software tool to facilitate variable-level equivalency and harmonization in research data: Leveraging the NIH Common Data Elements Repository to link concepts and measures in an open format

促进研究数据中变量级别等效性和协调性的软件工具：利用 NIH 通用数据元素存储库以开放格式链接概念和测量

• 批准号: 10821517
• 负责人: Dan Smith
• 金额: $ 27.55万
• 依托单位: ALGENTA TECHNOLOGIES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10821517
• 关键词: Aging; Algorithmic Analysis; Algorithms; Alzheimer' s disease related dementia; Archives; Characteristics; Collection; Common Data Element; Computer software; Data; Data Coordinating Center; Data Element; Data Files; Data Set; Data Sources; Databases; Development; Documentation; Effectiveness; Environment; FAIR principles; Funding; Individual; Libraries; Link; Machine Learning; Manuals; Maps; Measures; Meta-Analysis; Metadata; Modeling; Morphologic artifacts; National Institute on Aging; Outcome; Output; Pain; Phase; Process; Public Health; Questionnaires; Recommendation; Research; Research Personnel; Resources; Software Tools; Source; Standardization; Structure; Technology; Testing; Text; Time; United States; United States National Institutes of Health; aging population; artificial intelligence algorithm; cost; data harmonization; data management; data reduction; data sharing; human-in-the-loop; individual variation; interoperability; metadata standards; multidisciplinary; multiple datasets; prototype; repository; tool; tool development

Abstract The National Institute on Aging (NIA) supports numerous studies and archives that collect and disseminate critical data about the aging population of the United States. By supporting the collection and dissemination of longitudinal and multidisciplinary data, the NIA provides researchers the opportunity to measure change and stability in individuals over time, as well as to investigate aging phenomena from an integrated theoretical perspective. In both cases, equivalent or related variables must first be linked or merged before producing appropriately documented data products for eventual harmonization and analysis. The current aging research data environment provides many opportunities for linking similar topical datasets and harmonizing extant common variables, but few software tools are available to facilitate this resource-intensive task. The proposed project will demonstrate the feasibility of a guided harmonization software prototype by concording variables from three nationally representative NIA-funded studies (MIDUS, NHATS, NSHAP) and mapping them against extant data element concept sources such as the NIH Common Data Elements library to identify equivalent concepts and variables. The software prototype will use machine learning and advanced text analysis algorithms to guide the creation of concorded databases (variable crosswalks) that support harmonization and discoverability, both within and across aging-related statistical datasets. Additionally, the prototype will use an open-standards metadata framework to produce richly-described concordance databases that are interoperable, citable and FAIR. Colectica has a track record of creating open- standards based software tools that reduce data management burden by automatically extracting structured metadata from macro-level (study) and micro-level (variable) characteristics of aging studies. Specifically, the prototype will evaluate the feasibility of human-in-the-loop algorithms to operate as a “recommendation engine” to guide the concordance of potentially equivalent or similar variables among multiple datasets. The core hypothesis posits that the prototype will significantly decrease the labor, time, and resources required to create accurate and standardized concorded databases. To test this hypothesis, the research team will: construct and evaluate recommendation algorithms for variable concordance (Aim 1); establish metrics for measuring the accuracy and effectiveness of concordance (Aim 2); and create a user interface to test the recommendation engine, its functions, and associated inputs and outputs (Aim 3).

抽象的 国家老龄化研究所 (NIA) 支持大量研究和档案，收集和传播 有关美国人口老龄化的关键数据 通过支持收集和传播 纵向和多学科数据，NIA 为研究人员提供了衡量变化和 个体随时间的稳定性，以及从综合理论研究衰老现象 在这两种情况下，在生成之前必须首先链接或合并等效或相关变量。 适当记录的数据产品，用于最终协调和分析当前的老龄化研究。 数据环境为链接相似的主题数据集和协调现有数据集提供了许多机会 常见的变量，但很少有软件工具可用于完成这项资源密集型任务。 项目将通过协调变量来证明引导协调软件原型的可行性 来自 NIA 资助的三项具有全国代表性的研究（MIDUS、NHATS、NSHAP）并将其与 现有的数据元素概念来源，例如 NIH 通用数据元素库，用于识别等效项 该软件原型将使用机器学习和高级文本分析。 指导创建一致数据库（可变人行横道）的算法，以支持协调和 与衰老相关的统计数据集内部和之间的可发现性。 此外，该原型将使用开放标准元数据框架来生成描述丰富的 Colectica 具有创建开放式索引数据库的良好记录。 基于标准的软件工具，通过自动提取结构化数据来减轻数据管理负担 元数据来自老龄化研究的宏观层面（研究）和微观层面（变量）特征。 原型将评估人机循环算法作为“推荐引擎”运行的可行性 指导多个数据集中潜在等效或相似变量的一致性。 假设假设原型将显着减少创建所需的劳动力、时间和资源 为了检验这一假设，研究团队将： 构建并建立一致的数据库。 评估变量一致性的推荐算法（目标 1）；建立衡量变量一致性的指标； 一致性的准确性和有效性（目标 2）；并创建一个用户界面来测试推荐 引擎、其功能以及相关的输入和输出（目标 3）。