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.

项目总结

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