Machine Learning Models Identify Kids at Risk of Lead Poisoning

Machine learning can help public health officials identify children most at risk of lead poisoning, enabling them to concentrate their limited resources on preventing poisonings rather than remediating homes only after a child suffers elevated blood lead levels, a new study shows. Rayid Ghani , Distinguished Career Professor in Carnegie Mellon University's Machine Learning Department and Heinz College of Information Systems and Public Policy , said the Chicago Department of Public Health (CDPH) has implemented an intervention program based on the new machine learning model and Chicago hospitals are in the midst of doing the same. Other cities also are considering replicating the program to address lead poisoning, which remains a significant environmental health issue in the United States. In a study published Sept. 16 in the journal JAMA Network Open, Ghani and colleagues at the University of Chicago and CDPH report that their machine learning model is about twice as accurate in identifying children at high risk than previous, simpler models, and equitably identifies children regardless of their race or ethnicity. Elevated blood lead levels can cause irreversible neurological damage in children, including developmental delays and irritability. Lead-based paint in older housing is the typical source of lead poisoning.