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

抽象-整体瞄准中心 这一提议的人工智能、建模和信息学的愿景，用于营养指导和 系统(目标)中心是实施计算和数据科学的方法和工具，以推动 精确健康的营养，在某种程度上解释了所涉及的复杂系统。许多现有的数据集 包括无关数据，这使得它们在最好的情况下难以分析，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 以使它们更适合人工智能(AI)，并为一系列不同的分析做好准备。这不谋而合 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，可以折叠和提取与营养相关的数据，以创建人工智能数据集- 准备好进行一系列其他分析。精准健康营养(NPH)的首要目标 项目是“通过研究不同饮食中观察到的个体差异 饮食、基因、蛋白质、微生物组、新陈代谢等个体背景因素之间的相互作用。 考虑到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 现在需要的不是相关性，而是新的归罪方法。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。学习如何传递因果知识并对丢失的数据进行传递是至关重要的 为了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体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路径结合在一起，以了解 他们相互作用。基于代理的模型(ABM)可以帮助并充当“虚拟实验室”来预测差异有多大 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( 用于精确营养的虚拟人)是开发一种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，同时综合和计算 遗传、生理和行为因素之间的相互作用。然而，重点放在 仅靠个人并不足以解决NPH的所有方面。因此，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、经济和建成环境。A管理人员 协调核心将监督所有业务和一个试点项目。数据系统核心(DSC)将 利用CUNY、西点军校和国防部的大量计算资源 灵活的基于云的数据流架构和协作工作空间。A计算系统 CORE将提供资源和人员来支持DSC和工具开发/部署。

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.