ArticleJAMA network open2020
Validation of a Machine Learning Model to Predict Childhood Lead Poisoning.
Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 39 citations in OpenAlex.
- Applications of Machine Learning in the Research of Heavy Metal(loid)s-Related Risk: A Scoping Review of Methodology.Toxics · 2026Review
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Machine Learning Unveils Dietary Antioxidants as Influential Factors for Diabetes-Cancer Comorbidity: Insights From National Health and Nutrition Examination Survey.Food science & nutrition · 2026Article
- Modeling Elevated Children's Blood Lead Levels across Five U.S. States: A Statistical Approach to Improve Predictions and Understand Key Drivers.ACS environmental Au · 2026Article
- Development and internal evaluation of a clinical prediction model for blood lead re-elevation in lead poisoning patients: a retrospective cohort study.Frontiers in toxicology · 2026Article
- Development and validation of an explainable machine learning-based risk prediction model for obesity in Chinese children and adolescents: a population-based study.Frontiers in nutrition · 2026Article
- What's in Your Soil? A Citywide Investigation of the Importance of Soil Lead for Predicting Elevated Blood Lead Levels in Chicago.GeoHealth · 2025Article
- Lead exposure in the 21st century: Modeling a path from crisis to prevention.Eco-Environment & Health · 2025Article
- Artificial Intelligence Applications in Emergency Toxicology: Advancements and Challenges.Journal of medical Internet research · 2025Review
- Machine learning for predicting medical outcomes associated with acute lithium poisoning.Scientific reports · 2025Article
- Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making.European journal of pediatrics · 2025Article
- Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis.Briefings in bioinformatics · 2024Article
- Construction of a machine learning-based prediction model for unfavorable discharge outcomes in patients with ischemic stroke.Heliyon · 2024Article
- Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms.BMC medical informatics and decision making · 2024Article
- Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study.EClinicalMedicine · 2024Article
- Predicting blood lead in Uruguayan children: Individual- vs neighborhood-level ensemble learners.PLOS global public health · 2024Article
- Comparison of machine learning algorithms to predict intentional and unintentional poisoning risk factors.Heliyon · 2023Article
- Predicting Low-Level Childhood Lead Exposure in Metro Atlanta Using Ensemble Machine Learning of High-Resolution Raster Cells.International journal of environmental research and public health · 2023Article
- The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Childhood lead poisoning causes irreversible neurobehavioral deficits, but current practice is secondary prevention. Objective: To validate a machine learning (random forest) prediction model of elevated blood lead levels (EBLLs) by comparison with a parsimonious logistic regression. Design, Setting, and Participants: This prognostic study for temporal validation of multivariable prediction models used data from the Women, Infants, and Children (WIC) program of the Chicago Department of Public Health. Participants included a development cohort of children born from January 1, 2007, to December 31, 2012, and a validation WIC cohort born from January 1 to December 31, 2013. Blood lead levels were measured until December 31, 2018. Data were analyzed from January 1 to October 31, 2019. Exposures: Blood lead level test results; lead investigation findings; housing characteristics, permits, and violations; and demographic variables. Main Outcomes and Measures: Incident EBLL (≥6 μg/dL). Models were assessed using the area under the receiver operating characteristic curve (AUC) and confusion matrix metrics (positive predictive value, sensitivity, and specificity) at various thresholds. Results: Among 6812 children in the WIC validation cohort, 3451 (50.7%) were female, 3057 (44.9%) were Hispanic, 2804 (41.2%) were non-Hispanic Black, 458 (6.7%) were non-Hispanic White, and 442 (6.5%) were Asian (mean [SD] age, 5.5 [0.3] years). The median year of housing construction was 1919 (interquartile range, 1903-1948). Random forest AUC was 0.69 compared with 0.64 for logistic regression (difference, 0.05; 95% CI, 0.02-0.08). When predicting the 5% of children at highest risk to have EBLLs, random forest and logistic regression models had positive predictive values of 15.5% and 7.8%, respectively (difference, 7.7%; 95% CI, 3.7%-11.3%), sensitivity of 16.2% and 8.1%, respectively (difference, 8.1%; 95% CI, 3.9%-11.7%), and specificity of 95.5% and 95.1% (difference, 0.4%; 95% CI, 0.0%-0.7%). Conclusions and Relevance: The machine learning model outperformed regression in predicting childhood lead poisoning, especially in identifying children at highest risk. Such a model could be used to target the allocation of lead poisoning prevention resources to these children.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.