Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center

营养指导和系统人工智能、建模和信息学 (AIMINGS) 中心

基本信息

批准号：
10552675
负责人：
Bruce Y Lee
金额：
$ 129.75万
依托单位：
GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY
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：
2022-01-19 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10552675
关键词：
Accounting Address Algorithms All of Us Research Program Architecture Area Artificial Intelligence Behavioral Complex Computational Science Data Data Science Data Set Department of Defense Diet Dietary Practices Economics Face Food Patterns Funding Genes Genetic Goals Health Human Resources Individual Individual Differences Informatics Information Systems Knowledge Laboratories Learning Metabolism Methods Modeling Nutritional Pathway interactions Persons Physiological Precision Health Proteins Public Health Research Resources Skin Structure System United States National Institutes of Health Vision built environment clinical application cloud based computing resources contextual factors flexibility individual response insight microbiome novel strategies nutrition operation precision nutrition prediction algorithm preservation programs response social tool tool development virtual virtual human virtual laboratory

项目摘要

Abstract – Overall AIMINGS Center The vision of this proposed Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center is to implement computational and data science approaches and tools to advance nutrition for precision health in a way that accounts for the complex systems involved. Many existing data sets include extraneous data, making them difficult to analyze at best, and at worst, prone to generating misleading or biased insights. Thus, there is a need to for new approaches, methods, and tools to collapse and distill data to make them more Artificial Intelligence (AI)-ready and ready for a range of different analyses. This coincides with the goal of Project 1: to develop and utilize The Data Distiller for Precision Nutrition, a set of approached and tools that can collapse and distill nutrition-relevant data to create datasets that are AI- ready and ready for a range of other analyses. The first objective of the Nutrition for Precision Health (NPH) program is to “examine individual differences observed in response to different diets by studying the interactions between diet, genes, proteins, microbiome, metabolism and other individual contextual factors.” Given the type of missing data we face in nutrition, and the importance of establishing causal relationships rather than correlations, there is a need for new imputation methods. To address this, Project 2, the Causal Relationship Disentangler, will introduce new approaches for handling missing data while preserving causal structure. Learning how to transfer causal knowledge and doing so with missing data is critical for realizing the potential of nutrition for precision health. The NPH program’s other objectives are “to use AI to develop algorithms to predict individual responses to foods and dietary patterns,” and “to validate algorithms for clinical application.” This requires bringing different causal pathways together to understand how they interact. Agent-based models (ABMs) can help and serve as "virtual laboratories" to predict how different people may respond to a particular diet under different circumstances. Therefore, the goal of Project 3 (The Virtual Human for Precision Nutrition) is to develop an ABM tool that can help better understand and predict an individual's response to food and dietary patterns, while bringing together and accounting for the interactions between genetic, physiological, and behavioral factors. However, focusing on the individual alone will not be enough to address all aspects of NPH. Therefore, the Virtual Public Health Precision Nutrition Laboratory (Project 4) will develop ABMs that represent and account for the systems outside individuals such as their social, economic, and built environments. An Administrative and Coordination Core will oversee all operations and a pilot program. A Data Systems Core (DSC) will leverage the substantial computing resources of CUNY, West Point, and the Department of Defense to create a flexible cloud-based architecture for data flow and a collaborative workspace. A Computational Systems Core will provide resources and personnel to support the DSC and tool development/deployment.

摘要 - 整体目标中心这种提议的人工智能，建模和信息学的愿景，营养指导和系统（目标）中心是实施计算和数据科学方法和工具以推进精确健康的营养以涉及复杂系统的方式。许多现有数据集包括无关的数据，使它们充其量很难分析，最坏的情况很容易产生误导或有偏见的见解。这是需要新方法，方法和工具来崩溃和提炼数据使他们更加人工智能（AI），并准备好进行一系列不同的分析。这重合以项目1的目的为目标：要开发和利用数据蒸馏器进行精确营养，一组接近和可以崩溃和提炼与营养相关的数据的工具以创建AI-AI- 准备好并准备进行一系列其他分析。精确健康营养（NPH）的第一个目标程序是“通过研究来检查对不同饮食观察到的个体差异饮食，基因，蛋白质，微生物组，代谢和其他个人情境因素之间的相互作用。” 鉴于我们在营养中面临的缺失数据的类型以及建立因果关系的重要性而不是相关性，需要新的插补方法。为了解决这个问题，项目2，因果关系关系解开，将引入新的方法来处理丢失的数据，同时保存数据因果结构。学习如何转移因果知识并使用丢失的数据进行此操作至关重要实现营养潜力以进行精确健康。 NPH程序的其他目标是“使用 AI开发算法以预测对食物和饮食模式的个人反应”和“验证临床应用算法。“这需要将不同的因果途径融合在一起，以了解如何他们互动。基于代理的模型（ABM）可以帮助并充当“虚拟实验室”，以预测不同的不同在不同情况下，人们可能会对特定的饮食做出反应。因此，项目3的目标（用于精确营养的虚拟人）是开发一种ABM工具，该工具可以帮助更好地理解和预测个人对食物和饮食模式的反应，同时将对于遗传，物理和行为因素之间的相互作用。但是，专注于单独的个人不足以解决NPH的各个方面。因此，虚拟公共卫生精密营养实验室（项目4）将开发代表和解释的ABM 在个人，社会，经济和建筑环境等个人之外的系统。行政协调核心将监督所有操作和试点计划。数据系统核心（DSC）将利用CUNY，West Point和国防部的大量计算资源来创建一个基于云的灵活体系结构，用于数据流和协作工作区。计算系统核心将提供资源和人员来支持DSC和工具开发/部署。