Approaches for AI/ML Readiness for Wildfire Exposures.

项目总结 本申请是根据(NOSI)确定为NOT-CA-22-056提交的。 背景资料。家长资助(RF1AG071024)的具体目的是评估轻度认知障碍的风险 与野火相关的损害(MCI)、阿尔茨海默病(AD)和AD相关痴呆(ADRD) 颗粒物(PM2.5)(目标1)，以查明加剧疾病的个人和地区一级的易感因素 野火PM2.5与MCI和AD/ADRD的关系(AIM 2)，并估计MCI和AD/ADRD的风险 与居住在野火灾难附近的人以及特定的小组在多大程度上更好或更差有关 成果(目标3)。 作为在母公司R01的AIMS 1和AIMS 2中进行的工作的一部分，我们正在模拟每天暴露在野火中- 使用两阶段机器学习(ML)方法来确定特定的PM2.5水平。我们策划和处理了一大批 来自一系列来源的数据量，包括天气变量、卫星数据和环境保护 美国环保署(EPA)的监测数据，以便对野火特定的PM2.5水平进行建模。虽然我们已经花费了 在数据管理方面付出了相当大的努力，但我们并没有专注于使数据成为人工智能(AI)/ML 对我们自己的研究人员和更广泛的研究社区都是现成的和公开的。数据 有效的野火分析所需的来源是不同的、不太容易访问的，并且对AI/ML不友好 申请。尽管数据很丰富，而且可以通过美国机构公开获得，但获取数据并做好准备 对于任何研究人员来说，这都是一项重大的投资。 总体目标和目的。通过这项管理建议，我们计划与AI/ML建立新的协作 和哈佛大学的数据专家，他们的目标是改善浩瀚而广泛的数据源， 开发可复制的管道、注释、记录和处理数据，确保计算 可伸缩性，鼓励社区参与，并传播这些重要的AI/ML就绪数据集 野火PM2.5的预测被更广泛的研究界所接受。我们的具体目标是改善以下数据 AI/ML就绪(AIM 1)，使数据对AI/ML应用程序公开可用(AIM 2)，并演示 在AI/ML应用程序中转换数据以预测加州的野火PM2.5暴露(目标3)。 冲击力。最终的数据集将是AI/ML就绪的、可重现的，并将传播给广泛的用户基础。我们将建造 一个协作环境，允许内部和外部研究人员使用、贡献和改进 数据输入。这项工作将为我们的小组预测#年野火PM2.5暴露奠定基础。 整个美国和整个社区，并将加强母公司R01的目标。

PROJECT SUMMARY This application is being submitted in response to the (NOSI) identified as NOT-CA-22-056. Background. The specific aims of the parent grant (RF1AG071024) are to estimate the risk of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) and AD-related dementias (ADRD) associated with wildfire particulate matter (PM2.5) (Aim 1), to identify individual- and area-level susceptibility factors that exacerbate the association between wildfire PM2.5 and MCI and AD/ADRD (Aim 2), and to estimate the risk of MCI and AD/ADRD associated with living near a wildfire disaster and the extent to which specific sub-groups have better or worse outcomes (Aim 3). As part of the work conducted in Aims 1 and 2 of the parent R01, we are modeling daily exposure to wildfire- specific PM2.5 levels using a two-stage machine learning (ML) approach. We have curated and processed a large quantity of data from a range of sources including weather variables, satellite data, and Environmental Protection Agency (EPA) monitor data, in order to model wildfire specific PM2.5 levels. While we have expended considerable effort on the data curation, we have not focused on making the data Artificial Intelligence (AI)/ML ready and publicly available, both for our own researchers and for the broader research community. The data sources required for effective wildfire analysis are disparate, not very accessible, and unfriendly to AI/ML applications. Although the data is rich and publicly available through US agencies, acquiring it and preparing it for analysis presents a significant investment for any researcher. Overall Goals and Aims. With this administrative proposal, we plan to establish a new collaboration with AI/ML and data experts at Harvard University with the goals of improving the vast and wide range of data sources, developing reproducible pipelines, annotating, documenting, and processing the data, ensuring computational scalability, encouraging community engagement, and disseminating these important AI/ML ready datasets for the prediction of wildfire PM2.5 to a wider research community. Our specific aims are to improve the data for AI/ML readiness (Aim 1), make the data publicly available to AI/ML applications (Aim 2), and demonstrate the transformed data in an AI/ML application to predict wildfire PM2.5 exposure for California (Aim 3). Impact. The final datasets will be AI/ML ready, reproducible, and disseminated to a wide user base. We will build a collaborative environment allowing both internal and external researchers to use, contribute, and improve the data inputs. This work will serve as a foundation for our group in the prediction of wildfire PM2.5 exposures for the whole US and for the community and will strengthen the aims of the parent R01.