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

项目摘要 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FAIR（Findable， 可兼容、可互操作、可重用）数据标准。将不同的数据源整合在一起将需要 复杂的数据解决方案，拥有一个非常成功和可访问的HEAL数据共享。满足这些数据 目标带来两大挑战。第一个问题是，现有的孤立数据集尚无法 与其他限制可查找性和可访问性的数据结合。二是收集整理 预期的数据，以便可以确保数据质量和完整性，从而实现 数据。为了实现这一目标，本提案响应了有关使数据更加 机器学习/人工智能（ML/AI ready）。家长补助金的重点是创建一个研究 HEAL IMPOWR网络和更大的科学界的框架，以协调联合慢性 疼痛（CP）和阿片类药物使用障碍（OUD）数据。这份行政补充材料将这一使命扩展到 将NIH HEAL IMPOWR网络的范围扩展到现有和未来的CP和OUD。拟议的工作将 显著地深化和增强了在“治愈”计划和OUD数据中采用FAIR原则的方法， 网络和更大的NIH研究社区。它提高了父母补助金的严格性， 我们正在做的事情超出了NIH HEAL IMPOWR网络的数据相关性。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。在我们先前工作的基础上，本项目的总体目标是将CP和OUD数据移动到一个 通过利用现有数据集并为新项目开发工具，向FAIR迈进一步。总 该项目的假设是利用现有的CP & 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。一般的假设是 通过以下特定目标进行测试：（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 基于语义和语法分析的HEAL CDE注释。(2)调整工具， 支持现有和预期收集的HEAL CDE的ML/AI就绪性。首先，我们将调整我们以前的 开发工具来测量和评估疼痛/OUD CDE的语义距离。这将支持 通过使用SHAP建模评估定量距离来开发联邦迁移学习， 以前收集的数据。我们假设，这些工具将提供必要的基础设施， 开发ML/AI就绪数据。在目标1中，我们认为将现有数据集转换为ML/AI就绪将 加速现有和未来数据的协调，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 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。的 研究结果将为HEAL数据生态系统的进一步发展提供坚实的基础。

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.