Wake Forest IMPOWR Dissemination Education and Coordination Center (IDEA-CC)

维克森林 IMPOWR 传播教育和协调中心 (IDEA-CC)

• 批准号: 10593312
• 负责人: MEREDITH C. B. ADAMS
• 金额: $ 30.97万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10593312
• 关键词: Administrative Supplement; Architecture; Artificial Intelligence; Charge; Clinical; Clinical Pathways; Clinical Research; Clinical Trials; Common Data Element; Communities; Data; Data Analytics; Data Collection; Data Commons; Data Element; Data Set; Data Sources; Development; Ecosystem; Education; Ensure; FAIR principles; Funding; Future; Goals; Infrastructure; Knowledge; Libraries; Machine Learning; Maps; Measures; Mission; Modeling; Monoclonal Antibody R24; National Institute of Drug Abuse; Outcome; Pain; Pathway interactions; Patients; Performance; Persons; Phase; Public Health; Readiness; Research; Risk; Scientist; Semantics; Standardization; System; Techniques; Testing; United States National Institutes of Health; Variant; Work; base; chronic pain; chronic pain management; clinical research site; comorbidity; complex data; computer science; data dictionary; data ecosystem; data infrastructure; data quality; data quality/integrity; data resource; data reuse; data standards; diverse data; experience; federated learning; forest; health equity promotion; improved; interoperability; large scale data; meetings; opioid use disorder; parent grant; prevent; programs; prospective; syntax; tool; transfer learning

PROJECT SUMMARY The HEAL Data Ecosystem is working to collect data across its projects and networks to meet FAIR (Findable, Accessible, Interoperable, Reusable) data standards. Bringing diverse data sources together will require complex data solutions to have a highly successful and accessible HEAL data commons. Meeting these data goals brings two major challenges. The first is there are existing siloed datasets that are not yet able to be combined with other data limiting the findability and accessibility. The second is collecting and organizing prospective data so that one could assure data quality and integrity that allows for interoperability and reuse of the data. To accomplish this goal, this proposal responds to the request for strategies to make data more machine learning/artificial intelligence (ML/AI ready). The focus of the parent grant is to create a research framework for the HEAL IMPOWR network and larger scientific community to harmonize combined chronic pain (CP) and opioid use disorder (OUD) data. This administrative supplement expands this mission beyond the scope of the NIH HEAL IMPOWR network to existing and future CP and OUD. The proposed work will significantly deepen and augment approaches to FAIR principles in CP and OUD data for both the HEAL network and larger NIH research community. It enhances the rigor of the parent grant by improving the larger data relevance of what we are doing beyond the NIH HEAL IMPOWR network. The long-term goal is to build a HEAL Data Ecosystem that incorporates existing data and supports the integration of prospective CP and OUD data collection. Building on our prior work, the overall objective of this project is to move CP and OUD data one step closer to FAIR by leveraging existing datasets and developing tools for new projects. The general hypothesis of the project is that leveraging existing CP & OUD data and collecting new data using ML/AI data quality standards will accelerate the impact of the HEAL Data Ecosystem. The general hypothesis will be tested by the following specific aims: (1) Transform existing dataset by mapping chronic pain/OUD CDE to demonstrate use case for making existing siloed data into a ML/AI ready format by automatically suggesting HEAL CDE annotations for already collected data based on semantic and syntactic analysis. (2) Adapt tools to support ML/AI readiness for existing and prospectively collected HEAL CDE. First, we will adapt our previously developed tools to measure and assess the semantic distance for pain/OUD CDE. This will support the development of federated transfer learning by assessing the quantitative distances using SHAP modeling of previously collected data. We hypothesize that these tools will provide infrastructure necessary to successfully develop ML/AI ready data. In aim 1, we believe that transforming existing datasets to be ML/AI ready will accelerate the harmonization of existing and prospective data for a future HEAL Data Commons. In aim 2, the development of CDE tools will support the data infrastructure quality checks to support ML/AI. The expected outcome of this project is data optimization pipelines and tools to support the goal of ML/AI ready data. The results will provide a strong basis for further development of the HEAL Data Ecosystem.

项目概要 HEAL 数据生态系统正在努力收集其项目和网络中的数据，以满足公平 (Findable, 可访问、可互操作、可重用）数据标准。将不同的数据源整合在一起需要 复杂的数据解决方案，以拥有高度成功且可访问的 HEAL 数据共享。满足这些数据 目标带来两大挑战。首先是现有的孤立数据集尚无法 与限制可查找性和可访问性的其他数据相结合。二是收集整理 前瞻性数据，以便可以确保数据质量和完整性，从而实现互操作性和重用 数据。为了实现这一目标，本提案响应了使数据更加丰富的策略要求 机器学习/人工智能（ML/AI 就绪）。家长资助的重点是创建一项研究 HEAL IMPOWR 网络和更大的科学界协调综合慢性病的框架 疼痛（CP）和阿片类药物使用障碍（OUD）数据。该行政补充将这一使命扩展至超越 NIH HEAL IMPOWR 网络的范围涵盖现有和未来的 CP 和 OUD。拟议的工作将 显着深化和增强 HEAL 的 CP 和 OUD 数据中公平原则的方法 网络和更大的 NIH 研究社区。它通过改进更大的范围来提高家长补助金的严格性 我们在 NIH HEAL IMPOWR 网络之外所做的事情的数据相关性。长期目标是建立一个 HEAL数据生态系统，整合现有数据并支持未来CP和OUD的集成 数据收集。在我们之前的工作基础上，该项目的总体目标是将 CP 和 OUD 数据移至一处 通过利用现有数据集并为新项目开发工具，更接近 FAIR。将军 该项目的假设是利用现有的 CP 和 OUD 数据并使用 ML/AI 数据收集新数据 质量标准将加速 HEAL 数据生态系统的影响。一般假设是 通过以下具体目标进行测试：(1) 通过将慢性疼痛/OUD CDE 映射到 演示通过自动建议将现有孤立数据转换为 ML/AI 就绪格式的用例 基于语义和句法分析对已收集的数据进行 HEAL CDE 注释。 (2) 调整工具 支持现有和未来收集的 HEAL CDE 的 ML/AI 准备。首先，我们将调整之前的 开发了测量和评估疼痛/OUD CDE 语义距离的工具。这将支持 通过使用 SHAP 模型评估定量距离来开发联邦迁移学习 之前收集的数据。我们假设这些工具将提供成功所需的基础设施 开发 ML/AI 就绪数据。在目标 1 中，我们相信将现有数据集转变为 ML/AI 就绪将 加速现有数据和未来数据的协调，以实现未来的 HEAL 数据共享。在目标 2 中， CDE 工具的开发将支持数据基础设施质量检查，以支持 ML/AI。预期的 该项目的成果是数据优化管道和工具，以支持 ML/AI 就绪数据的目标。这 结果将为HEAL数据生态系统的进一步发展提供坚实的基础。