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

• 批准号: 10379613
• 负责人: Rachel Clipp
• 金额: $ 24.99万
• 依托单位: KITWARE, INC.
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10379613
• 关键词: Address; Adolescent; Applied Research; Carcinogens; Chemicals; Collaborations; Community Health; Complex; Computer Models; Computer Simulation; Custom; Data; Data Set; Data Storage and Retrieval; Deposition; Devices; Effectiveness; Electronic Nicotine Delivery Systems; Electronic cigarette; Encapsulated; Environmental Health; Fruit; Health; Human; Individual; Ingestion; Internet; Link; Machine Learning; Marketing; Medical; Modeling; Online Systems; Phase; Physiological; Physiology; Population; Population Study; Postdoctoral Fellow; Probability; Process; Research Personnel; Risk; Risk Assessment; Safety; Small Business Innovation Research Grant; System; Techniques; Testing; Time; Toxic effect; Toxin; Training; Validation; Visualization; base; cancer risk; computational toxicology; computer framework; cost; design; dosimetry; electronic cigarette use; experience; health data; innovation; large datasets; machine learning algorithm; machine learning model; multilayer perceptron; nicotine exposure; novel; open source; simulation; smoking cessation; tool; toxicant; usability; vaping

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.

抽象的 近年来，电子烟和电子烟设备的使用日益增多，引起了人们的担忧 健康社区。虽然安全性尚未得到充分表征，但这些设备是相互关联的 戒烟工作、针对青少年的有针对性的营销活动和添加剂， 例如促进使用的水果口味。已收集实验数据进行调查 毒性、致死率和癌症风险。但此类数据的空白和难度 收集大型数据集会给风险评估计算带来挑战。计算型 预测化学和毒素分布、沉积和剂量测定的模型已被建立 成功展示；然而，对于大型的计算要求是令人望而却步的 人口研究。我们假设用一个替代昂贵的计算模型 机器学习模型将以较低的计算成本产生准确的风险评估 该过程可以推广到其他环境健康数据。 该项目是 Kitware, Inc. 和 Applied Research 之间的密切合作 联营公司（ARA）。 Kitware 团队拥有丰富的计算开发经验 用于模拟、存储、管理和分析大型数据集的生理学模型 医疗和健康相关分析以及机器学习技术。我们开发了一个 开源平台 Girder，用于创建与大型数据集和数据相关的定制工作流程 机器学习分析。 ARA 在计算建模和毒性方面拥有丰富的经验 毒素和化学物质的沉积和剂量测定及其相关机制的分析 与电子烟和电子烟设备。在这个项目中，我们建议结合各方的专业知识 Kitware 和 ARA 的团队为大型数据集存储和开发定制工作流程 分别整合和分析机器学习技术和结果。我们将 使用使用生成的合成数据证明工作流程的有效性 模型的计算框架。第一期项目的具体目标是：（1）生成 使用高保真计算建模方法的大型数据集； (2) 创建优化 用于摄取大型环境健康数据集以用于机器学习计算的工作流程 风险评估； (3) 开发机器学习模型来替代第一原理模型 预测环境健康的风险评估。