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: 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
• 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

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 该提议的人工智能、建模和信息学的愿景是用于营养指导和 系统（AIMINGS）中心旨在实施计算和数据科学方法和工具，以推进 以一种考虑到许多现有数据集的方式实现精准健康的营养。 包含无关的数据，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 因此，需要新的方法、方法和工具来分解和提炼数据。 使它们更好地为人工智能（AI）做好准备并准备好进行一系列不同的分析。 项目 1 的目标是：开发和利用 The Data Distiller for Precision Nutrition，这是一套 可以分解和提取营养相关数据以创建人工智能数据集的方法和工具 为精准健康营养 (NPH) 的首要目标做好准备。 该计划的目的是“通过研究不同饮食的反应来检查观察到的个体差异” 饮食、基因、蛋白质、微生物组、新陈代谢和其他个体背景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类型以及建立因果关系的重要性 为了解决这个问题，需要新的插补方法，而不是相关性，项目 2，因果关系。 关系解缠器将引入新的方法来处理丢失的数据，同时保留 学习如何迁移因果知识以及如何处理缺失的数据至关重要。 NPH 计划的其他目标是“利用营养促进精准健康”。 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路径结合起来以了解如何进行。 它们相互作用，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（The）。 Virtual Human for Precision Nutrition）旨在开发一种 ABM 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，同时汇总和计算 然而，关注遗传、生理和行为因素之间的相互作用。 仅靠个人不足以解决 NPH 的所有方面，因此，虚拟公共卫生。 精准营养实验室（项目 4）将开发 ABM，代表并解释 个人之外的系统，例如社会、经济和建筑环境。 协调核心将监督所有运营，数据系统核心（DSC）将负责监督。 利用纽约市立大学、西点军校和国防部的大量计算资源来创建 用于数据流和协作工作空间的灵活的基于云的架构。 Core 将提供资源和人员来支持 DSC 和工具开发/部署。

项目成果

Vulnerability or Resiliency? A Two-Wave Panel Analysis of Social Network Factors Associated with Glycemic Levels among Mexican Immigrants in the Bronx, NYC, Before and During COVID-19.

脆弱性还是弹性？

• 发表时间: 2024-02
• 作者: Flórez, Karen R;Hwang, Neil S;Hernández, Maria;Verdaguer, Sandra;Derose, Kathryn P;de la Haye, Kayla
• 通讯作者: de la Haye, Kayla

Translating digital anthropometry measurements obtained from different 3D body image scanners.

转换从不同 3D 身体图像扫描仪获得的数字人体测量结果。

• 发表时间: 2023-09
• 影响因子: 4.7
• 作者: Ashby, Nicholas;Jake LaPorte, G;Richardson, Daniel;Scioletti, Michael;Heymsfield, Steven B;Shepherd, John A;McGurk, Michael;Bustillos, Brenda;Gist, Nicholas;Thomas, Diana M
• 通讯作者: Thomas, Diana M

Machine learning modeling practices to support the principles of AI and ethics in nutrition research.

机器学习建模实践支持营养研究中的人工智能和伦理原则。

• 发表时间: 2022-12-02
• 影响因子: 6.1
• 作者: Thomas, Diana M.;Kleinberg, Samantha;Brown, Andrew W.;Crow, Mason;Bastian, Nathaniel D.;Reisweber, Nicholas;Lasater, Robert;Kendall, Thomas;Shafto, Patrick;Blaine, Raymond;Smith, Sarah;Ruiz, Daniel;Morrell, Christopher;Clark, Nicholas
• 通讯作者: Clark, Nicholas

Natural language processing: fast forwarding research to the "good stuff": Natural language processing for nutrition.

自然语言处理：将研究快速推进到“好东西”：用于营养的自然语言处理。

• 发表时间: 2023-03
• 作者: Lindquist; Joseph M
• 通讯作者: Joseph M

Research gaps and opportunities in precision nutrition: an NIH workshop report.

精准营养的研究差距和机遇：NIH 研讨会报告。

• DOI: 10.1093/ajcn/nqac237
• 发表时间: 2022-09-02
• 期刊: The American journal of clinical nutrition
• 作者: Bruce Y Lee;J. Ordovás;E. Parks;Cheryl Anderson;A. Barabási;S. Clinton;K. de la Haye;V. Duffy;P. Franks;Elizabeth M Ginexi;K. Hammond;Erin C. Hanlon;Michael Hittle;E. Ho;A. Horn;R. Isaacson;P. Mabry;S. Malone;Corby K. Martin;J. Mattei;S. Meydani;Lorene M. Nelson;M. Neuhouser;Brendan Parent;N. Pronk;H. Roche;S. Saria;F. Scheer;E. Segal;M. Sevick;T. Spector;Linda Van Horn;K. Varady;V. S. Voruganti;Marie F Martinez
• 通讯作者: Marie F Martinez