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Tracking health disparities through natural-language processing

Author

Listed:
  • Wieland, M.L.
  • Wu, S.T.
  • Kaggal, V.C.
  • Yawn, B.P.

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.

Suggested Citation

  • Wieland, M.L. & Wu, S.T. & Kaggal, V.C. & Yawn, B.P., 2013. "Tracking health disparities through natural-language processing," American Journal of Public Health, American Public Health Association, vol. 103(3), pages 448-449.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2012.300943_7
    DOI: 10.2105/AJPH.2012.300943
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