ArticleFrontiers in public health2022
Predictive models for small-for-gestational-age births in women exposed to pesticides before pregnancy based on multiple machine learning algorithms.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Predicting Low Birth Weight in Big Cities in the United States Using a Machine Learning Approach.International journal of environmental research and public health · 2025Article
- Antenatal prediction of small for gestational age at birth based on four birthweight standards using machine learning algorithms.Frontiers in artificial intelligence · 2025Article
- Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.BMC medical informatics and decision making · 2024Article
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10 authors.
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Abstract
Background: The association between prenatal pesticide exposures and a higher incidence of small-for-gestational-age (SGA) births has been reported. No prediction model has been developed for SGA neonates in pregnant women exposed to pesticides prior to pregnancy. Methods: A retrospective cohort study was conducted using information from the National Free Preconception Health Examination Project between 2010 and 2012. A development set ( Results: 757 neonates in total were analyzed. SGA occurred in 12.9% ( Conclusions: Machine learning algorithms can develop satisfactory tools for SGA prediction in mothers exposed to pesticides prior to pregnancy, which might become a tool to predict SGA neonates in the high-risk population.
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