A Computationally Efficient Approach to Predict Population Risk with Machine Learning

摘要 近年来，电子烟和蒸发设备的使用越来越多，这是一个令人担忧的问题 健康社区。虽然安全性还没有完全确定，但这些设备是相互关联的 戒烟努力，针对青少年的有针对性的营销活动，以及添加剂， 例如水果的味道，促进了使用。收集了实验数据进行了研究 毒性、致命性和致癌风险。然而，这类数据中的差距和困难 收集大数据集会给风险评估计算带来挑战。计算型 用来预测化学和毒素分布、沉积和剂量学的模型已经被提出 成功演示；但是，计算要求对于大型 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，并 这一过程可以推广到其他环境健康数据。 该项目是Kitware，Inc.和应用研究公司密切合作的项目 联营公司(Ara)。Kitware团队拥有丰富的计算开发经验 用于大数据集的模拟、存储、管理和分析的生理模型 与医疗和健康相关的分析和机器学习技术。我们已经开发出一种 开源平台BELDER，用于创建与大型数据集和 机器学习分析。ARA在计算模型和毒性方面有丰富的经验 毒素和化学品的沉积和剂量测定及其相关机制的分析 使用电子烟和蒸发设备。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和 分别结合和分析机器学习技术和结果。我们会 使用使用 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数据集；(2)创建优化的 获取用于机器学习的大型环境健康数据集以进行计算的工作流 风险评估；以及(3)开发机器学习模型，以取代基本原理模型和 预测环境健康风险评估。

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.