Tracking health disparities through natural-language processing

M.L. Wieland, S.T. Wu, V.C. Kaggal and B.P. Yawn

American Journal of Public Health, 2013, vol. 103, issue 3, 448-449

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.

Keywords: algorithm; article; electronic medical record; ethnology; health disparity; human; natural language processing; refugee; Somalia; statistics; United States, Algorithms; Electronic Health Records; Health Status Disparities; Humans; Minnesota; Natural Language Processing; Refugees; Somalia (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:aph:ajpbhl:10.2105/ajph.2012.300943_7

DOI: 10.2105/AJPH.2012.300943

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