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Using machine learning to investigate the influence of the prenatal chemical exposome on neurodevelopment of young children
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“Machine learning on application to 406 maternal -child dyads, found prenatal chemicals (phthalates, metals, and other chemicals) and socio-demographic factors ranked highly as predictors of neurodevelopmental outcomes at age 2. 58”
From Environmental roots of neonatal health: how social and structural exposures shape early-life outcomes (2026) · cites this paper as [58]
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Relief-based feature selection: Introduction and review
Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.07.014 · Cited by 1,311
Glynn, A., Berger, U., Bignert, A., Ullah, S., Aune, M., Lignell, S., & Darnerud, P. O. (2012) · Environmental Science & Technology · doi:10.1021/es301168c · Cited by 417
Exposure to environmental endocrine disruptors and child development
Meeker, J. D. (2012) · Archives of Pediatrics & Adolescent Medicine · doi:10.1001/archpediatrics.2012.241 · Cited by 294
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Cited by
Nawaz, K., Babata, K., Scheid, L., Bitar, L., Ortiz, N. V., Kota, S., & Chalak, L. F. (2026) · Pediatric Research · doi:10.1038/s41390-026-05139-5 · Cited by 1
Metadata
- Authors
- England-Mason, G., MacEachern, S. J., Amador, K., Soomro, M. H., Reardon, A. J. F., MacDonald, A. M., Kinniburgh, D. W., Letourneau, N., Giesbrecht, G. F., Martin, J. W., Forkert, N. D., & Dewey, D.
- Venue
- NeuroToxicology
- Publisher
- Elsevier BV
- Published
- 2025-05
- Volume
- 108
- Pages
- 218-230
- ISSN
- 0161-813X
- Cited by
- 9
- References
- 3
- Language
- en
- Licence
- Funders
- National Institute of Environmental Health Sciences
- Alberta Children's Hospital Research Institute
- Canada Research Chairs
- Canadian Institutes of Health Research (HTA-472411, MOP-123535, PG-425614)
- Alberta Innovates - Health Solutions
- National Institutes of Health
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