Approaches for AI/ML Readiness for Wildfire Exposures.
针对野火暴露的 AI/ML 准备方法。
基本信息
- 批准号:10593837
- 负责人:
- 金额:$ 33.69万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:3-DimensionalAerosolsAlzheimer&aposs DiseaseAlzheimer&aposs disease related dementiaAreaArtificial IntelligenceCaliforniaCensusesCodeCollaborationsCommunitiesComputer softwareDataData ProtectionData SetData SourcesDevelopmentDisastersEnsureEnvironmental WindExposure toFAIR principlesFire - disastersFoundationsFutureGoalsHumidityIndividualInvestmentsLocationMachine LearningMetadataMeteorologyModelingMonitorNational Oceanic and Atmospheric AdministrationOpticsOutcomeOutputParentsParticulate MatterPredispositionProcessReadinessReproducibilityResearchResearch PersonnelRisk EstimateSmokeSourceSubgroupSystemTechniquesTemperatureTestingTrainingUnited States Environmental Protection AgencyUnited States National Aeronautics and Space AdministrationUniversitiesWeatherWildfireWorkbasecollaborative environmentcommunity engagementcomputerized data processingdata curationdata formatdata repositorydesignfine particleshazardimprovedmachine learning algorithmmachine learning modelmachine learning predictionmild cognitive impairmentnovelparent grantpublic health researchremote sensingrepositoryresponsespatiotemporaltemporal measurementtool
项目摘要
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
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:5
- 作者:
- 通讯作者:
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:
- 影响因子:0
- 作者:McBrien,Heather;Rowland,SebastianT;Benmarhnia,Tarik;Tartof,SaraY;Steiger,Benjamin;Casey,JoanA
- 通讯作者:Casey,JoanA
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{{ truncateString('Joan A Casey', 18)}}的其他基金
2023 Regional ISEE NAC Meeting, Corvallis, OR
2023 年区域 ISEE NAC 会议,俄勒冈州科瓦利斯
- 批准号:
10683564 - 财政年份:2023
- 资助金额:
$ 33.69万 - 项目类别:
Short and long-term consequences of wildfires for Alzheimer's disease and related dementias
野火对阿尔茨海默病和相关痴呆症的短期和长期后果
- 批准号:
10824706 - 财政年份:2021
- 资助金额:
$ 33.69万 - 项目类别:
Historical social and environmental determinants of memory decline and dementia among U.S. older adults.
美国老年人记忆力衰退和痴呆的历史社会和环境决定因素。
- 批准号:
10301899 - 财政年份:2021
- 资助金额:
$ 33.69万 - 项目类别:
Historical social and environmental determinants of memory decline and dementia among U.S. older adults
美国老年人记忆力下降和痴呆症的历史社会和环境决定因素
- 批准号:
10824083 - 财政年份:2021
- 资助金额:
$ 33.69万 - 项目类别:
The Impact of Unconventional Natural Gas Development on Maternal, Perinatal, and Childhood Health: an Electronic Health Record Approach
非常规天然气开发对孕产妇、围产期和儿童健康的影响:电子健康记录方法
- 批准号:
10200037 - 财政年份:2019
- 资助金额:
$ 33.69万 - 项目类别:
The Impact of Unconventional Natural Gas Development on Maternal, Perinatal, and Childhood Health: an Electronic Health Record Approach
非常规天然气开发对孕产妇、围产期和儿童健康的影响:电子健康记录方法
- 批准号:
10016282 - 财政年份:2019
- 资助金额:
$ 33.69万 - 项目类别:
The Impact of Unconventional Natural Gas Development on Maternal, Perinatal, and Childhood Health: an Electronic Health Record Approach
非常规天然气开发对孕产妇、围产期和儿童健康的影响:电子健康记录方法
- 批准号:
9933124 - 财政年份:2019
- 资助金额:
$ 33.69万 - 项目类别:
The impact of unconventional natural gas development on maternal, perinatal, and childhood health: An electronic health record approach
非常规天然气开发对孕产妇、围产期和儿童健康的影响:电子健康记录方法
- 批准号:
9314971 - 财政年份:2017
- 资助金额:
$ 33.69万 - 项目类别:
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