Bridge2AI: Salutogenesis Data Generation Project

Bridge2AI：Salutogenesis 数据生成项目

基本信息

批准号：
10885481
负责人：
Sally Liu Baxter
金额：
$ 799.69万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10885481
关键词：
Address Affect Artificial Intelligence Asian Atlases Awareness Behavioral Black race Bridge to Artificial Intelligence Classification Clinical Cohort Studies Collaborations Communities Complex Consultations Data Data Collection Data Set Development Diabetes Mellitus Disease Equity Ethics FAIR principles Funding Future Future Generations Generations Goals Health Health Promotion Hispanic Human Resources Institution Insulin Latinx Learning Machine Learning Modeling Molecular Native Americans Non-Insulin-Dependent Diabetes Mellitus Organ Participant Pathologic Pathway interactions Patients Persons Physiological Population Heterogeneity Process Research Research Design Research Personnel Scientist Severity of illness Source Text Time Training Tribes United States National Institutes of Health Work cohort data reuse data sharing design improved insight legal implication multimodal data programs recruit social social implication

项目摘要

The Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) project is one of the data generation projects in the NIH Common Fund’s Bridge2AI program. The project seeks to create a flagship ethically-sourced dataset to enable future generations of artificial intelligence/machine learning (AI/ML) research to provide critical insights into type 2 diabetes mellitus (T2DM), including salutogenic pathways to return to health. The ability to understand and affect the course of complex, multi-organ diseases such as T2DM has been limited by a lack of well-designed, high quality, large, and inclusive multimodal datasets. The team of investigators will aim to collect a cross-sectional dataset of 4,000+ people and longitudinal data from 10% of the study cohort across the US. An equal number of Black, Hispanic/LatinX, Asian, and White participants will be recruited and the study cohort will be balanced for diabetes disease stage. Data collection will be specifically designed to permit downstream pseudotime manifold analysis, an approach used to predict disease trajectories by collecting and learning from complex, multimodal data from participants with differing disease severity (normal to insulin-dependent T2DM). The long-term objective for this project is to develop a foundational dataset in diabetes, agnostic to existing classification criteria or biases, which can be used to reconstruct a temporal atlas of T2DM development and reversal towards health (i.e., salutogenesis). Six cross-disciplinary project modules involving teams located across eight institutions will work together to develop this flagship dataset. All data will be optimized for downstream AI/ML research and made publicly available. This project will also create a roadmap for ethical and equitable research that focuses on the diversity of the research participants and the workforce involved at all stages of the research process (study design and data collection, curation, analysis, and sharing and collaboration). The AI-READI project will also engage in a tribal consultation to address barriers and facilitators of participation with the goal of collecting similar data within a Native American cohort in an ethical and respectful manner. Specific aims include 1) Collect and share the dataset for AI/ML research according to the Findable, Accessible, Interoperable, Reusable (FAIR) data principles, 2) Create a model for developing diverse and representative datasets, and 3) Increase access to and quality of AI/ML research by recruiting and training personnel with diverse backgrounds.

人工智能准备且公平的糖尿病见解（AI-Readi）项目是NIH Common Fund基金的Bridge2AI计划中的数据生成项目之一。该项目旨在创建一个具有道德道德的旗舰数据集，以使后代人工智能/机器学习（AI/ML）研究提供对2型糖尿病（T2DM）的关键见解，包括用于恢复健康的敬意途径。缺乏精心设计，高质量，大型和包容性的多模式数据集的限制，能够理解和影响复杂的多器官疾病（例如T2DM）的过程。调查人员团队的目标是收集来自美国10％的研究队列的4,000多人和纵向数据的横断面数据集。将招募相等数量的黑色，西班牙裔/拉丁裔，亚洲和白人参与者，研究队列将在糖尿病阶段保持平衡。数据收集将专门设计用于允许下游伪歧管分析，该方法用于通过从具有区分疾病严重程度（正常到胰岛素依赖性T2DM）的参与者中收集和从复杂的多模式数据中收集和学习来预测疾病轨迹。该项目的长期目标是开发糖尿病中的基础数据集，不可知现有的分类标准或偏见，可用于重建T2DM开发的临时地图集，并逆转健康（即致敬）。六个涉及位于八个机构的团队的六个跨学科项目模块将共同开发此旗舰数据集。所有数据将用于下游AI/ML研究，并公开可用。该项目还将为道德和公平研究创建一个路线图，该项目的重点是研究参与者的多样性以及研究过程的各个阶段（研究设计和数据收集，策划，分析以及共享和协作）所涉及的劳动力。 AI-Readi项目还将进行部落咨询，以解决参与的障碍和促进者，目的是以道德和尊重的方式在美国原住民队列中收集类似的数据。具体目的包括1）根据可发现的，可访问的，可互操作的，可重复使用的（公平）数据原理，收集和共享AI/ML研究的数据集，2）为开发潜水员和代表性数据集创建一个模型，以及3）通过招募和培训潜水员的人群来提高AI/ML研究的访问和质量，并培训AI/ML研究的质量。