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 提交本申请以响应标识为NOT-CA-22-056的（NOSI）。 背景父母补助金（RF 1AG 071024）的具体目标是估计轻度认知障碍的风险。 与野火相关的阿尔茨海默病（AD）和AD相关性痴呆（ADRD） （目标1），以确定加剧 野火PM2.5与MCI和AD/ADRD（目标2）之间的关联，并估计MCI和AD/ADRD的风险 与野火灾害附近的生活有关，以及特定子群体的生活状况更好或更差的程度 （目标3）。 作为父R 01的目标1和2中进行的工作的一部分，我们正在模拟每天暴露于野火的情况- 使用两阶段机器学习（ML）方法来确定PM2.5水平。我们精心策划和处理了一大批 大量的数据来自一系列来源，包括天气变量、卫星数据和环境保护 美国环保署（EPA）监测数据，以模拟野火特定的PM2.5水平。虽然我们已经花费了 尽管我们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 准备好并公开提供给我们自己的研究人员和更广泛的研究社区。数据 有效的野火分析所需的资源是不同的，不是很容易获得，对AI/ML不友好 应用.尽管这些数据丰富，并且可以通过美国机构公开获得， 对于任何研究人员来说，分析都是一项重大投资。 总体目标和宗旨。通过这项行政提案，我们计划与AI/ML建立新的合作关系。 以及哈佛大学的数据专家，他们的目标是改善庞大而广泛的数据源， 开发可复制的管道，注释，记录和处理数据，确保计算 可扩展性，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，以更广泛的研究社区。我们的具体目标是改善数据， AI/ML准备（目标1），使数据公开提供给AI/ML应用程序（目标2），并展示 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）。 冲击最终的数据集将是AI/ML就绪的，可复制的，并传播给广泛的用户群。我们将建立 一个协作的环境，允许内部和外部的研究人员使用，贡献，并提高 数据输入。这项工作将作为我们小组预测野火PM2.5暴露的基础， 整个美国和社区，并将加强父R 01的目标。

项目成果

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

野火烟雾曝光和急诊科的头痛访问：2006 - 2020年加利福尼亚州的病例分解分析。

• 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