Bridge2AI: Salutogenesis Data Generation Project

Bridge2AI:Salutogenesis 数据生成项目

基本信息

  • 批准号:
    10885481
  • 负责人:
  • 金额:
    $ 799.69万
  • 依托单位:
  • 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

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.
AI-READI项目是美国国立卫生研究院共同基金Bridge2AI项目中的数据生成项目之一。该项目寻求创建一个基于伦理的旗舰数据集,以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,包括恢复健康的有益途径。由于缺乏设计良好的、高质量、大容量和包容性的多模式数据集,理解和影响诸如T2 DM等复杂的多器官疾病的过程的能力一直受到限制。研究团队的目标是收集4000多人的横断面数据集和全美10%的研究队列的纵向数据。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,并对糖尿病疾病阶段的研究队列进行平衡。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(正常到胰岛素依赖型T2 DM)的参与者的复杂、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一个与现有分类标准或偏见无关的糖尿病基础数据集,可用于重建T2 DM发展和向健康逆转(即健康发生)的时间图谱。涉及八个机构的团队的六个跨学科项目模块将共同开发这一旗舰数据集。所有数据都将针对下游AI/ML研究进行优化,并公开提供。该项目还将制定一份伦理和公平研究路线图,侧重于研究过程所有阶段(研究设计和数据收集、管理、分析以及共享和合作)研究参与者和工作人员的多样性。AI-Readi项目还将进行部落协商,以消除参与的障碍和促进者,目标是以道德和尊重的方式在美洲原住民队列中收集类似数据。具体目标包括1)根据可查找、可访问、可互操作、可重复使用(公平)数据的原则收集和共享AI/ML研究的数据集,2)创建一个开发多样化和有代表性的数据集的模型,以及3)通过招聘和培训具有不同背景的人员来增加AI/ML研究的机会和质量。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Sally Liu Baxter其他文献

Sally Liu Baxter的其他文献

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{{ truncateString('Sally Liu Baxter', 18)}}的其他基金

PAGE-G: Precision Approach combining Genes and Environment in Glaucoma
PAGE-G:青光眼基因与环境相结合的精准方法
  • 批准号:
    10797646
  • 财政年份:
    2023
  • 资助金额:
    $ 799.69万
  • 项目类别:
Bridge2AI: Salutogenesis Data Generation Project
Bridge2AI:Salutogenesis 数据生成项目
  • 批准号:
    10858583
  • 财政年份:
    2022
  • 资助金额:
    $ 799.69万
  • 项目类别:
Bridge2AI: Salutogenesis Data Generation Project
Bridge2AI:Salutogenesis 数据生成项目
  • 批准号:
    10471118
  • 财政年份:
    2022
  • 资助金额:
    $ 799.69万
  • 项目类别:
Short-Term Research training In Vision and Eye health (STRIVE)
视觉和眼睛健康短期研究培训 (STRIVE)
  • 批准号:
    10615857
  • 财政年份:
    2022
  • 资助金额:
    $ 799.69万
  • 项目类别:
Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention
多模态人工智能预测青光眼进展和手术干预
  • 批准号:
    10677890
  • 财政年份:
    2022
  • 资助金额:
    $ 799.69万
  • 项目类别:
Short-Term Research training In Vision and Eye health (STRIVE)
视觉和眼睛健康短期研究培训 (STRIVE)
  • 批准号:
    10409942
  • 财政年份:
    2022
  • 资助金额:
    $ 799.69万
  • 项目类别:
Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention
多模态人工智能预测青光眼进展和手术干预
  • 批准号:
    10504041
  • 财政年份:
    2022
  • 资助金额:
    $ 799.69万
  • 项目类别:
Multi-modal Health Information Technology Innovations for Precision Management of Glaucoma
青光眼精准管理的多模式健康信息技术创新
  • 批准号:
    10018290
  • 财政年份:
    2020
  • 资助金额:
    $ 799.69万
  • 项目类别:
Multi-modal Health Information Technology Innovations for Precision Management of Glaucoma
青光眼精准管理的多模式健康信息技术创新
  • 批准号:
    10260459
  • 财政年份:
    2020
  • 资助金额:
    $ 799.69万
  • 项目类别:
Multi-modal Health Information Technology Innovations for Precision Management of Glaucoma
青光眼精准管理的多模式健康信息技术创新
  • 批准号:
    10437231
  • 财政年份:
    2020
  • 资助金额:
    $ 799.69万
  • 项目类别:

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