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

• 批准号: 10386497
• 负责人: Bruce Y Lee
• 金额: $ 121.25万
• 依托单位: GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-19 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10386497
• 关键词: Accounting; Address; Administrative Coordination; 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; Gene Proteins; Genetic; Goals; Health; Human Resources; Individual; Individual Differences; Informatics; Information Systems; Knowledge; Laboratories; Learning; Metabolism; Methods; Modeling; Nutritional; Pathway interactions; Persons; Physiological; Precision Health; Public Health; Research; Resources; Skin; Structure; System; United States National Institutes of Health; Vision; base; 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

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-总体目标中心 这一建议的人工智能，建模和信息学的愿景，营养指导和 系统（AIMINGS）中心是实施计算和数据科学方法和工具，以推进 营养对精准健康的影响，以一种解释所涉及的复杂系统的方式。许多现有的数据集 包括无关数据，这使得它们在最好情况下难以分析，在最坏的情况下，容易产生误导 或有偏见的见解。因此，需要新的途径、方法和工具来折叠和提取数据 使它们更人工智能（AI）-准备好并准备好进行一系列不同的分析。这与 项目1的目标是：开发和利用精准营养数据蒸馏器，一套 方法和工具，可以折叠和提取营养相关的数据，以创建AI数据集- 准备好进行一系列的其他分析。精准健康营养（NPH） 该项目的目的是“通过研究不同的饮食习惯， 饮食、基因、蛋白质、微生物组、新陈代谢和其他个体环境因素之间的相互作用。” 鉴于我们在营养方面面临的缺失数据的类型，以及建立因果关系的重要性， 需要的不是相关性，而是新的估算方法。为了解决这个问题，项目2，因果关系 Relationship Disentangler将引入新的方法来处理丢失的数据，同时保留 因果结构学习如何转移因果知识并在缺失数据的情况下这样做至关重要 实现营养对精准健康的潜力。NPH计划的其他目标是“使用 人工智能开发算法来预测个人对食物和饮食模式的反应，并验证 临床应用的算法”这需要将不同的因果途径结合在一起，以了解 它们相互作用。基于代理的模型（ABM）可以帮助并作为“虚拟实验室”来预测如何不同 人们可能会在不同的情况下对特定的饮食做出反应。因此，项目3的目标是： 精确营养虚拟人）是开发一个ABM工具，可以帮助更好地理解和 预测一个人对食物和饮食模式的反应， 基因、生理和行为因素之间的相互作用。然而，重点放在 单靠个人是不足以解决NPH的所有方面的问题的。虚拟公共卫生 精确营养实验室（项目4）将开发代表和说明 个人之外的系统，如他们的社会，经济和建筑环境。行政 和协调核心将监督所有业务和试点计划。数据系统核心（DSC）将 利用纽约市立大学、西点和国防部的大量计算资源， 一个灵活的基于云的架构，用于数据流和协作工作空间。计算系统 核心将提供资源和人员，以支持DSC和工具开发/部署。