Li Liu

Automated and accurate identification of refugees in healthcare databases is a critical first step to investigate healthcare needs of this vulnerable population and improve health disparities. In this study, we developed a machine‐learning method, named refugee identification system (RIS) to address this need. We curated a data set consisting of 103 refugees and 930 non‐refugees in Arizona. We compiled de‐identified individual‐level information including age, primary language, and noise‐masked home address, state‐level refugee resettlement statistics, and world language statistics. We then performed feature engineering to convert language and masked address into quantitative features. Finally, we built a random forest model to classify refugee and non‐refugees. RIS achieved high classification accuracy (overall accuracy=0.97, specificity=0.99, sensitivity=0.85, positive predictive value=0.88, negative predictive value=0.98, and area under receiver operating characteristic curve=0.98). RIS is customizable for refugee identification outside Arizona. Its application enables large‐scale investigation of refugee healthcare needs and improvement of health disparities.