Machine learning identifies traffic-related pollutant mixtures as potential factors suggestive of association with osteoporosis in a cross-sectional analysis of NHANES.
- DOI
- 10.1097/md.0000000000048884
- Published
- 2026 May 29
- Container
- Medicine
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1097/md.0000000000048884,
title = {Machine learning identifies traffic-related pollutant mixtures as potential factors suggestive of association with osteoporosis in a cross-sectional analysis of NHANES.},
author = {Wang Y and Deng M and Xu Y and Zha Z and Feng Y and Zhuang T and He J and Peng H and Wu W},
year = {2026},
journal = {Medicine},
doi = {10.1097/md.0000000000048884},
url = {https://doi.org/10.1097/md.0000000000048884}
}RIS
TY - JOUR TI - Machine learning identifies traffic-related pollutant mixtures as potential factors suggestive of association with osteoporosis in a cross-sectional analysis of NHANES. AU - Wang Y AU - Deng M AU - Xu Y AU - Zha Z AU - Feng Y AU - Zhuang T AU - He J AU - Peng H AU - Wu W PY - 2026 JO - Medicine DO - 10.1097/md.0000000000048884 UR - https://doi.org/10.1097/md.0000000000048884 ER -
APA
Y, W., M, D., Y, X., Z, Z., Y, F., T, Z., J, H., H, P., & W, W. (2026). Machine learning identifies traffic-related pollutant mixtures as potential factors suggestive of association with osteoporosis in a cross-sectional analysis of NHANES.. Medicine. https://doi.org/10.1097/md.0000000000048884
Source records
- pubmed · retrieved 2026-09-26T08:24:52.406Z