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.

糖尿病洞察的人工智能就绪和公平地图集 (AI-READI) 项目是 NIH 共同基金 Bridge2AI 计划的数据生成项目之一，该项目旨在创建一个符合道德的旗舰数据集，以支持未来几代人工智能/。机器学习 (AI/ML) 研究为 2 型糖尿病 (T2DM) 提供重要见解，包括恢复健康的有益途径、理解和影响复杂性过程的能力、 T2DM 等多器官疾病由于缺乏精心设计、高质量、大型且包容性的多模式数据集而受到限制。研究团队的目标是收集 4,000 多人的横截面数据集和来自 10 个人的纵向数据。将招募同等数量的黑人、西班牙裔/拉丁裔、亚洲人和白人参与者，并针对糖尿病疾病阶段对数据收集进行专门设计，以允许下游假时间流形。分析，一种通过收集和学习不同疾病严重程度（正常到胰岛素依赖型 T2DM）参与者的复杂多模式数据来预测疾病轨迹的方法。该项目的长期目标是开发糖尿病的基础数据集，与现有的分类标准或偏差无关，可用于重建 T2DM 发展和向健康逆转（即有益作用）的时间图谱，涉及八个机构的团队将共同开发六个跨学科项目模块。该旗舰数据集的所有数据都将针对下游人工智能/机器学习研究进行优化，并公开发布，该项目还将创建一个道德和公平研究的路线图，重点关注研究参与者和参与各个阶段的劳动力的多样性。 AI-READI 项目还将进行部落咨询，以解决参与的障碍和促进因素，目标是在美洲原住民群体中收集类似的数据。以道德和尊重的方式实现的具体目标包括1) 根据可查找、可访问、可互操作、可重用 (FAIR) 数据原则收集和共享用于人工智能/机器学习研究的数据集，2) 创建用于开发多样化和代表性数据集的模型，以及 3) 提高人工智能的访问和质量/通过招募和培训具有不同背景的人员进行机器学习研究。