A Novel Application of Non-Negative Matrix Factorization to the Prediction of the Health Status of Undocumented Immigrants.

ABSTRACT: Introduction: Undocumented immigrants (UIs) in the United States are less likely to be able to afford health insurance. As a result, UIs often lack family doctors and are rarely involved in annual screening programs, which makes estimating their health status remarkably challenging. This is especially true if the laboratory results from limited screening programs fail to provide sufficient clinical information. Methods: To address this issue, we have developed a machine learning model based on the non-negative matrix factorization technique. The data set we used for model training and testing was obtained from the 2004 cost-free hepatitis B screening program at the Omni Health Center located in Plano, Texas. Total 300 people were involved, with 199 identified as UIs. Resul

SUBMITTER: Li J 

PROVIDER: S-EPMC8742291 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature