A Computationally Efficient Approach to Predict Population Risk with Machine Learning

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）开发机器学习模型来取代第一原理模型， 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.