Predicting cumulative lead (Pb) exposure using the Super Learner algorithm.

ABSTRACT: Chronic lead (Pb) exposure causes long term health effects. While recent exposure can be assessed by measuring blood lead (half-life 30 days), chronic exposures can be assessed by measuring lead in bone (half-life of many years to decades). Bone lead measurements, in turn, have been measured non-invasively in large population-based studies using x-ray fluorescence techniques, but the method remains limited due to technical availability, expense, and the need for licensing radioactive materials used by the instruments. Thus, we developed prediction models for bone lead concentrations using a flexible machine learning approach--Super Learner, which combines the predictions from a set of machine learning algorithms for better prediction performance. The study population included 695 men in th

SUBMITTER: Wang X 

PROVIDER: S-EPMC10160242 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature