Approaches for AI/ML Readiness for Wildfire Exposures.

针对野火暴露的 AI/ML 准备方法。

• 批准号: 10593837
• 负责人: Joan A Casey
• 金额: $ 33.69万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10593837
• 关键词: 3-Dimensional; Aerosols; Alzheimer' s Disease; Alzheimer' s disease related dementia; Area; Artificial Intelligence; California; Censuses; Code; Collaborations; Communities; Computer software; Data; Data Protection; Data Set; Data Sources; Development; Disasters; Ensure; Environmental Wind; Exposure to; FAIR principles; Fire - disasters; Foundations; Future; Goals; Humidity; Individual; Investments; Location; Machine Learning; Metadata; Meteorology; Modeling; Monitor; National Oceanic and Atmospheric Administration; Optics; Outcome; Output; Parents; Particulate Matter; Predisposition; Process; Readiness; Reproducibility; Research; Research Personnel; Risk Estimate; Smoke; Source; Subgroup; System; Techniques; Temperature; Testing; Training; United States Environmental Protection Agency; United States National Aeronautics and Space Administration; Universities; Weather; Wildfire; Work; base; collaborative environment; community engagement; computerized data processing; data curation; data format; data repository; design; fine particles; hazard; improved; machine learning algorithm; machine learning model; machine learning prediction; mild cognitive impairment; novel; parent grant; public health research; remote sensing; repository; response; spatiotemporal; temporal measurement; tool

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.

项目摘要 该申请是针对（NOSI）确定为非CA-22-056的。 背景。父母赠款的具体目的（RF1AG071024）是估计轻度认知的风险 与野火相关的障碍（MCI）和阿尔茨海默氏病（AD）和与广告相关的痴呆症（ADRD） 颗粒物（PM2.5）（目标1），以识别加剧的个体和面积水平的敏感性因素 Wildfire PM2.5与MCI和AD/ADRD（AIM 2）之间的关联，并估算MCI和AD/ADRD的风险 与在野火灾难附近生活以及特定子群体更好或更糟的程度有关 结果（目标3）。 作为父母R01的目标1和2进行的工作的一部分，我们正在对每天暴露于野火建模 - 使用两阶段机器学习（ML）方法的特定PM2.5级别。我们已经策划并处理了一个大型 来自一系列来源的数据数量，包括天气变量，卫星数据和环境保护 代理商（EPA）监视数据，以模拟特定于野火的PM2.5级别。当我们探索 在数据策划方面的巨大努力，我们没有专注于制作数据人工智能（AI）/ML 为我们自己的研究人员和更广泛的研究社区准备就绪和公开可用。数据 有效野火分析所需的来源是不同的，不是很容易访问，并且不友善地对AI/ML 申请。尽管数据富裕且通过美国机构公开获得，并获取并准备它 为了分析，对任何研究人员提供了一项巨大的投资。 总体目标和目标。通过此行政建议，我们计划与AI/ML建立新的合作 哈佛大学的数据专家的目标是改善广泛和广泛的数据源， 开发可重复的管道，注释，记录和处理数据，确保计算 可扩展性，鼓励社区参与，并传播这些重要的AI/ML准备就绪数据集 对广泛的研究社区的野火PM2.5的预测。我们的具体目的是改善数据 AI/ML准备就绪（AIM 1），将数据公开可用于AI/ML应用程序（AIM 2），并演示 在AI/ML应用中转换数据，以预测加利福尼亚州的野火PM2.5暴露（AIM 3）。 影响。最终数据集将准备好，可重现，并将其传播到广泛的用户群中。我们将建造 一个协作环境，允许内部和外部研究人员使用，贡献和改善 数据输入。这项工作将为我们小组的基础，以预测野火PM2.5 整个美国和社区，将加强父母R01的目标。

项目成果

Wildfire smoke exposure and emergency department visits for headache: A case-crossover analysis in California, 2006-2020.

• DOI: 10.1111/head.14442
• 发表时间: 2023-01
• 期刊: Headache
• 影响因子: 5

Wildfire Exposure and Health Care Use Among People Who Use Durable Medical Equipment in Southern California.

南加州使用耐用医疗设备的人们的野火暴露和医疗保健使用情况。

• DOI: 10.1097/ede.0000000000001634
• 发表时间: 2023
• 期刊: Epidemiology (Cambridge, Mass.)
• 作者: McBrien,Heather;Rowland,SebastianT;Benmarhnia,Tarik;Tartof,SaraY;Steiger,Benjamin;Casey,JoanA
• 通讯作者: Casey,JoanA