title: Tracking health disparities through natural-language processing.
abstract: Health disparities and solutions are heterogeneous within and among racial and ethnic groups, yet existing administrative databases lack the granularity to reflect important sociocultural distinctions. We measured the efficacy of a natural-language-processing algorithm to identify a specific immigrant group. The algorithm demonstrated accuracy and precision in identifying Somali patients from the electronic medical records at a single institution. This technology holds promise to identify and track immigrants and refugees in the United States in local health care settings.
authors: Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN 55904, USA. wieland.mark@mayo.edu
authors: Mark L
authors: ML
authors: Wieland
authors: Stephen T
authors: ST
authors: Wu
authors: Vinod C
authors: VC
authors: Kaggal
authors: Barbara P
authors: BP
authors: Yawn
mesh_terms: D000465:Algorithms; D057286:Electronic Health Records / Q000706:statistics & numerical data; D054624:Health Status Disparities; D006801:Humans; D008910:Minnesota / Q000453:epidemiology; D009323:Natural Language Processing; D012036:Refugees / Q000706:statistics & numerical data; D012998:Somalia / Q000208:ethnology