Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology.
Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environm...
| Publicado en: | Environmental Health: A Global Access Science Source Vol. 17; no. 1 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
4/17/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129116142&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129116142 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1476069X 1CNO jtl: Environmental Health: A Global Access Science Source issn: 1476069X maglogo: N pubinfo: dt: 4/17/2018 vid: 17 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 129116142 129116142 NLM29665858 129116142 10.1186/s12940-018-0386-x NLM29665858 129116142 ppct: 1 formats: tig: atl: Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology. aug: au: VoPham, Trang Hart, Jaime E. Laden, Francine Chiang, Yao-Yi affil: Department of Epidemiology, Harvard T.H. Chan School of Public Health 677 Huntington Avenue 02115 Boston MA USA sug: subj: Environmental Exposure Environmental Monitoring Methods Environmental Health Methods Artificial Intelligence Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology. pubtype: Academic Journal doctype: research Journal Article ougenre: Unknown language: English refInfo: holdings: @attributes: islocal: N |
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