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:10386497
- 负责人:
- 金额:$ 121.25万
- 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:2022-01-19 至 2026-12-31
- 项目状态:未结题
- 来源:
- 关键词:AccountingAddressAdministrative CoordinationAlgorithmsAll of Us Research ProgramArchitectureAreaArtificial IntelligenceBehavioralComplexComputational ScienceDataData ScienceData SetDepartment of DefenseDietDietary PracticesEconomicsFaceFood PatternsFundingGene ProteinsGeneticGoalsHealthHuman ResourcesIndividualIndividual DifferencesInformaticsInformation SystemsKnowledgeLaboratoriesLearningMetabolismMethodsModelingNutritionalPathway interactionsPersonsPhysiologicalPrecision HealthPublic HealthResearchResourcesSkinStructureSystemUnited States National Institutes of HealthVisionbasebuilt environmentclinical applicationcloud basedcomputing resourcescontextual factorsflexibilityindividual responseinsightmicrobiomenovel strategiesnutritionoperationprecision nutritionprediction algorithmpreservationprogramsresponsesocialtooltool developmentvirtualvirtual humanvirtual 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
项目成果
期刊论文数量(0)
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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{{ truncateString('Bruce Y Lee', 18)}}的其他基金
Simulating the Spread and Control of Multiple MDROs Across a Network of Different Nursing Homes
模拟多个 MDRO 在不同疗养院网络中的传播和控制
- 批准号:
10549492 - 财政年份:2023
- 资助金额:
$ 121.25万 - 项目类别:
Project 3: The Virtual Human for Precision Nutrition
项目 3:精准营养虚拟人
- 批准号:
10552681 - 财政年份:2022
- 资助金额:
$ 121.25万 - 项目类别:
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 - 财政年份:2022
- 资助金额:
$ 121.25万 - 项目类别:
Project 4: Virtual Public Health Precision Nutrition Laboratory
项目4:虚拟公共卫生精准营养实验室
- 批准号:
10386502 - 财政年份:2022
- 资助金额:
$ 121.25万 - 项目类别:
Project 4: Virtual Public Health Precision Nutrition Laboratory
项目4:虚拟公共卫生精准营养实验室
- 批准号:
10552687 - 财政年份:2022
- 资助金额:
$ 121.25万 - 项目类别:
Project 3: The Virtual Human for Precision Nutrition
项目 3:精准营养虚拟人
- 批准号:
10386501 - 财政年份:2022
- 资助金额:
$ 121.25万 - 项目类别:
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