Validation of a Machine Learning Model to Predict Childhood Lead Poisoning
| Autor: | Raed Mansour, Cortland Lohff, Nikhil Prachand, Rayid Ghani, Joe Walsh, Eric Potash, Emile Jorgensen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Male
Logistic regression Machine learning computer.software_genre Pediatrics Risk Assessment Sensitivity and Specificity Lead poisoning Resource Allocation Machine Learning Interquartile range Preventive Health Services Medicine Humans Original Investigation Health Care Rationing Receiver operating characteristic medicine.diagnostic_test business.industry Research General Medicine medicine.disease Regression United States Lead Poisoning Online Only Logistic Models Child Preschool Cohort Blood lead level Female Artificial intelligence business computer Predictive modelling |
| Zdroj: | JAMA Network Open |
| ISSN: | 2574-3805 |
| Popis: | This prognostic study validates a machine learning (random forest) prediction model of elevated blood lead levels by comparing with a parsimonious logistic regression among children in a Women, Infants, and Children cohort. Key Points Question How does a machine learning model compare with logistic regression in predicting childhood lead poisoning during infancy? Findings This prognostic study of 6812 children in a Women, Infants, and Children cohort in Chicago, Illinois, used blood lead level surveillance data, housing characteristics, and demographic characteristics to predict lead poisoning risk. Using predictors from a range of spatiotemporal scales, a random forest model significantly outperformed a parsimonious logistic regression. Meaning These findings suggest that machine learning can be used to more precisely predict risk of elevated blood lead levels to guide allocation of prevention resources to children most at risk. 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|