Validation of a Machine Learning Model to Predict Childhood Lead Poisoning.

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

SUBMITTER: Potash E 

PROVIDER: S-EPMC7495240 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature