Bridge2AI: Salutogenesis Data Generation Project

Bridge2AI：Salutogenesis 数据生成项目

• 批准号: 10471118
• 负责人: Sally Liu Baxter
• 金额: $ 783.8万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 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/10471118
• 关键词: Artificial Intelligence; Atlases; Data Set; Disease; Health; Text; multimodality

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Bridge 2AI计划的数据生成项目之一。该项目旨在创建一个基于道德的旗舰数据集，使未来几代人工智能/机器学习（AI/ML）研究能够提供对2型糖尿病（T2 DM）的关键见解，包括恢复健康的健康途径。由于缺乏精心设计、高质量、大型和包容性的多模式数据集，理解和影响T2 DM等复杂多器官疾病病程的能力受到限制。研究团队的目标是收集4,000多人的横断面数据集和来自美国10%研究队列的纵向数据。将招募相同数量的黑人、西班牙裔/拉丁裔、亚裔和白色受试者，研究队列将在糖尿病分期方面保持平衡。数据收集将专门设计为允许下游伪时间流形分析，这是一种通过收集和学习来自不同疾病严重程度（正常至胰岛素依赖型T2 DM）受试者的复杂多模态数据来预测疾病轨迹的方法。该项目的长期目标是开发一个糖尿病基础数据集，对现有的分类标准或偏差不可知，可用于重建T2 DM发展和健康逆转的时间图谱（即，Salutogenesis）。六个跨学科项目模块涉及八个机构的团队，将共同开发这个旗舰数据集。所有数据都将针对下游AI/ML研究进行优化，并公开提供。该项目还将为道德和公平研究制定路线图，重点关注研究参与者的多样性和参与研究过程各个阶段的劳动力（研究设计和数据收集，策展，分析，共享和协作）。AI-READI项目还将参与部落协商，以解决参与的障碍和促进因素，目的是以道德和尊重的方式在美洲原住民群体中收集类似数据。具体目标包括：1）根据可发现、可解释、可互操作、可重用（FAIR）数据原则，收集和共享AI/ML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; 3）通过招募和培训具有不同背景的人员，提高AI/ML研究的可访问性和质量。