A Computationally Efficient Approach to Predict Population Risk with Machine Learning
通过机器学习预测人口风险的高效计算方法
基本信息
- 批准号:10379613
- 负责人:
- 金额:$ 24.99万
- 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:2022-02-25 至 2024-01-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAdolescentApplied ResearchCarcinogensChemicalsCollaborationsCommunity HealthComplexComputer ModelsComputer SimulationCustomDataData SetData Storage and RetrievalDepositionDevicesEffectivenessElectronic Nicotine Delivery SystemsElectronic cigaretteEncapsulatedEnvironmental HealthFruitHealthHumanIndividualIngestionInternetLinkMachine LearningMarketingMedicalModelingOnline SystemsPhasePhysiologicalPhysiologyPopulationPopulation StudyPostdoctoral FellowProbabilityProcessResearch PersonnelRiskRisk AssessmentSafetySmall Business Innovation Research GrantSystemTechniquesTestingTimeToxic effectToxinTrainingValidationVisualizationbasecancer riskcomputational toxicologycomputer frameworkcostdesigndosimetryelectronic cigarette useexperiencehealth datainnovationlarge datasetsmachine learning algorithmmachine learning modelmultilayer perceptronnicotine exposurenovelopen sourcesimulationsmoking cessationtooltoxicantusabilityvaping
项目摘要
Abstract
The growing use of e-cigarettes and vaping devices in recent years is a concern for the
health community. While the safety has not yet been fully characterized, these devices are linked
to smoking cessation efforts, targeted marketing campaigns towards adolescents, and additives,
such as fruit flavors, that promote use. Experimental data has been collected to investigate
toxicity, lethality, and risk for cancer. However, the gaps in this type of data and the difficulty
collecting large datasets leads to challenges with risk assessment calculations. Computational
modeling to predict chemical and toxin distribution, deposition, and dosimetry has been
successfully demonstrated; however, the computational requirements are prohibitive for large
population studies. We hypothesize that replacing expensive computational models with a
machine learning model will produce accurate risk assessment for a low computational cost and
that this process can be generalized for other environmental health data.
This project is a close collaboration between Kitware, Inc. and Applied Research
Associates, Inc. (ARA). The Kitware team has extensive experience developing computational
physiology models for use in simulation, storage, curation, and analysis of large dataset for
medical and health related analysis, and machine learning techniques. We have developed an
open source platform, Girder, for creating customized workflows related to large datasets and
machine learning analysis. ARA has extensive experience in computational modeling and toxicity
analysis for the deposition and dosimetry of toxins and chemicals and the mechanisms associated
with e-cigarettes and vaping devices. In this project, we propose combining the expertise of the
teams at Kitware and ARA to develop customized workflow for large data set storage and
incorporating and analyzing machine learning techniques and results, respectively. We will
demonstrate this effectiveness of the workflow using synthetic data generated using a
computational framework of models. The specific aims of the Phase I project are: (1) Generate
large datasets using high-fidelity computational modeling approaches; (2) Create an optimized
workflow for ingesting large environmental health datasets for use in machine learning to calculate
risk assessment; and (3) Develop a machine learning model to replace first principles models and
predict risk assessment for environmental health.
摘要
近年来,电子烟和vaping设备的使用越来越多,这是一个令人担忧的问题。
健康社区。虽然安全性尚未得到充分表征,但这些器械
戒烟努力,针对青少年的营销活动,以及添加剂,
例如水果香料,促进使用。实验数据已收集调查
毒性、致命性和癌症风险。然而,这类数据的差距和困难
收集大型数据集会给风险评估计算带来挑战。计算
预测化学品和毒素分布、沉积和剂量测定的建模,
成功地证明;然而,计算要求是禁止大
人口研究。我们假设,用一个
机器学习模型将以较低的计算成本产生准确的风险评估,
这个过程可以推广到其他环境健康数据。
该项目是Kitware,Inc.和应用研究
associates公司(ARA)。Kitware团队在开发计算
生理学模型,用于模拟、存储、管理和分析大型数据集,
医疗和健康相关分析以及机器学习技术。我们已经开发了一个
开源平台Girder,用于创建与大型数据集相关的自定义工作流,
机器学习分析ARA在计算建模和毒性方面拥有丰富的经验
毒素和化学品的沉积和剂量测定及其相关机制的分析
电子烟和电子烟设备。在这个项目中,我们建议将
Kitware和ARA的团队为大型数据集存储开发定制的工作流程,
分别结合和分析机器学习技术和结果。我们将
使用合成数据证明工作流程的有效性,
模型的计算框架。第一阶段项目的具体目标是:(1)产生
使用高保真计算建模方法的大型数据集;(2)创建优化的
工作流程,用于输入大型环境健康数据集,用于机器学习计算
风险评估;以及(3)开发机器学习模型以取代第一原理模型,
预测环境健康风险评估。
项目成果
期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Rachel Clipp其他文献
Rachel Clipp的其他文献
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{{ truncateString('Rachel Clipp', 18)}}的其他基金
Surgical Simulator for Improving Skill Proficiency and Resilience
用于提高技能熟练程度和恢复能力的手术模拟器
- 批准号:
10276881 - 财政年份:2021
- 资助金额:
$ 24.99万 - 项目类别:
Surgical Simulator for Improving Skill Proficiency and Resilience
用于提高技能熟练程度和恢复能力的手术模拟器
- 批准号:
10668406 - 财政年份:2021
- 资助金额:
$ 24.99万 - 项目类别:
Surgical Simulator for Improving Skill Proficiency and Resilience
用于提高技能熟练程度和恢复能力的手术模拟器
- 批准号:
10468965 - 财政年份:2021
- 资助金额:
$ 24.99万 - 项目类别:
Optimizing the Pulse Physiology Engine to Meet Medical Simulation Community Needs
优化脉冲生理学引擎以满足医学模拟社区的需求
- 批准号:
10609281 - 财政年份:2021
- 资助金额:
$ 24.99万 - 项目类别:
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