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Uncertainty-Aware Machine Learning for Ambient Air-Pollution Exposure Surfaces in Biomedical Research: From Data Fusion to Neuroepidemiology-Ready Inference Google Scholar

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Betekhtin AA

Volume6-Issue12
Dates: Received: 2025-12-12 | Accepted: 2025-12-29 | Published: 2025-12-30
Pages: 1996-2001

Abstract

Ambient air pollution remains a major, preventable driver of cardio metabolic and neurological disease burden. For biomedical studies, the central methodological bottleneck is not only prediction of pollutant concentrations, but trustworthy exposure assessment: leakage-safe validation, Uncertainty Quantification (UQ), transportable models in low-monitor regions, and transparent propagation of exposure uncertainty into health-effect estimates. This mini-review synthesizes recent advances in global and regional PM2.5 mapping, spatiotemporal deep learning, virtual monitoring stations, and gap-filling, and links these developments to the rapidly expanding evidence on dementia risk. We provide a practical checklist and worked calculations that translate modern Machine Learning (ML) exposure products into epidemiology-ready inputs.

FullText HTML FullText PDF DOI: 10.37871/jbres2245

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Copyright

© 2025 Betekhtin AA. Distributed under Creative Commons CC-BY 4.0

How to cite this article

Betekhtin AA. Uncertainty-Aware Machine Learning for Ambient Air-Pollution Exposure Surfaces in Biomedical Research: From Data Fusion to Neuroepidemiology-Ready Inference. J Biomed Res Environ Sci. 2025 Dec 30; 6(12): 1996-2001. doi: 10.37871/jbres2245, Article ID: JBRES2245, Available at: https://www.jelsciences.com/articles/jbres2245.pdf

Subject area(s)

Public Health
Epidemiology

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