Overview

An article published in Public Health Reports entitled, "Machine-Learning Algorithms to Code Public Health Spending Accounts," compares performances of machine-learning algorithms to determine if machines provide a faster, cheaper alternative to manual classification of public health expenditures. Analysis indicates that machine-learning algorithms can be a time and cost-savings tool. To read the article in full, click here. 

DISCLAIMER: The authors received financial support from the de Beaumont Foundation supporting this work. The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review. While this publication was funded by the de Beaumont Foundation, it is shared on this website because the findings are related to their grant funded by the Robert Wood Johnson Foundation and administered by the Systems for Action National Signature Research Program.