An Interface Ontology for Alzheimer's Disease Research

阿尔茨海默病研究的界面本体

• 批准号: 10261454
• 负责人: Licong Cui
• 金额: $ 19.5万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10261454
• 关键词: Address; Affect; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease related dementia; Alzheimer' s disease therapeutic; American; Area; Automobile Driving; Clinical Data; Clinical Research; Clinical Trials; Cohort Studies; Collection; Common Data Element; Communities; Data; Data Element; Data Science; Data Set; Development; Disease; Evaluation; Funding; Future; Generations; Goals; Grain; Growth; Infrastructure; Lead; Learning; Memory impairment; Metadata; Modality; Modeling; National Institute on Aging; Neurologic; Online Systems; Ontology; Patients; Performance; Privacy; Process; Public Health; Reporting; Reproducibility; Research; Research Personnel; Resources; Semantics; Sleep; Software Tools; Standardization; Strategic Planning; Subgroup; System; Terminology; Testing; Translations; United States National Institutes of Health; Web Ontology Language; Work; base; case control; cohort; comparative; daily functioning; data access; data exploration; data integration; data management; data resource; data sharing; data sharing networks; data standards; experience; human data; human old age (65+); improved; informatics tool; innovation; interoperability; metadata standards; nervous system disorder; neuroimaging; novel; prevent; query tools; success; tool; usability; web site; web-based tool

PROJECT SUMMARY A key barrier in Alzheimer’s disease (AD) research is the traditional data access workflow that requires a hypothesis prior to accessing patient data, rather than a workflow that begins with data exploration while protecting privacy. Existing data access interfaces for AD data resources allow researchers to simply explore data and build queries without the need for the user to understand how the data is stored. However, such interfaces have not achieved usability approaching the levels of those for consumer websites. The development of effective tools to support AD research data exploration requires standardized AD terminologies and data standards (or metadata). Existing efforts to standardize AD-related metadata include the Common Alzheimer’s Disease Research Ontology (CADRO), developed by the National Institute on Aging and the Alzheimer’s Association, to enable integration and comparative analysis of AD research portfolios for strategic planning and coordination. The Alzheimer's Disease Therapeutic Area User Guide (TAUG-Alzheimer's), has been developed by the Clinical Data Interchange Standards Consortium (CDISC) and the Coalition Against Major Diseases (CAMD), to improve the efficiency and learning from clinical trials in AD. Although they are important metadata resources for collecting and managing data, these existing AD terminologies and data standards are not designed, and thus are not sufficient, to be directly usable for developing data exploration tools and interfaces for AD research. We propose to develop a novel Interface Ontology for AD research (ADIO) to support web-based data faceted exploration through two Specific Aims. In Aim 1 we will develop ADIO and model a comprehensive collection of AD-related biomedical concepts which will be directly used for driving web-based data exploration tools. In Aim 2 we will develop ADIO-DE, a directly applicable, web-based data exploration tool for AD cohort discovery and test ADIO-DE using the National Alzheimer’s Coordinating Center (NACC) and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets. Anticipated results from this study will break new ground in web-based tools and capitalize on available data resources to accelerate AD research. We expect ADIO, ADIO-DE and their future versions to become an invaluable resource for the AD research community. The long-term goal of this study is to create data exploration systems for NACC, ADNI and other related AD data resources through data science innovations to transform user experience with a new generation of data interaction modalities.

项目总结 阿尔茨海默病(AD)研究中的一个关键障碍是传统的数据访问工作流，它需要 在访问患者数据之前的假设，而不是从数据探索开始的工作流 保护隐私。现有的AD数据资源数据访问接口允许研究人员简单地探索 数据和构建查询，而无需用户了解数据是如何存储的。然而，这样的 界面的可用性还没有达到消费者网站的水平。这个 开发支持AD研究数据探索的有效工具需要标准化的AD术语 和数据标准(或元数据)。现有标准化AD相关元数据的工作包括通用的 阿尔茨海默病研究本体论(CADRO)，由国家老龄化研究所和 阿尔茨海默病协会，以实现对AD研究组合的整合和比较分析，以实现战略 规划和协调。阿尔茨海默病治疗区用户指南(TAUG-阿尔茨海默氏症)，有 由临床数据交换标准联盟(CDISC)和反对 重大疾病(CAMD)，提高AD临床试验的效率和学习。尽管他们是 收集和管理数据的重要元数据资源，这些现有的AD术语和数据 标准不是专门设计的，因此不足以直接用于开发数据探索 AD研究的工具和界面。我们建议开发一种新的用于AD研究的接口本体 (ADIO)通过两个具体目标支持基于网络的数据方面的探索。在目标1中，我们将制定 ADIO和MODEL全面收集了与AD相关的生物医学概念，将直接用于 推动基于网络的数据探索工具。在目标2中，我们将开发Adio-DE，一种直接适用的、基于Web的 使用国家阿尔茨海默病协调中心的AD队列发现和测试Adio-DE的数据探索工具 中心(NACC)和阿尔茨海默病神经成像倡议(ADNI)的数据集。由此产生的预期结果 研究将在基于Web的工具方面开辟新的天地，并利用可用的数据资源来加速AD 研究。我们期待Adio、Adio-DE及其未来版本成为AD的无价资源 研究社区。这项研究的长期目标是为NACC、ADNI创建数据探索系统 和其他相关AD数据资源通过数据科学创新，以全新的方式转变用户体验 数据交互模式的生成。