通过自然语言处理追踪健康差距。
Tracking health disparities through natural-language processing.
机构信息
Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN 55904, USA.
出版信息
Am J Public Health. 2013 Mar;103(3):448-9. doi: 10.2105/AJPH.2012.300943. Epub 2013 Jan 17.
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.
摘要
健康差异及其解决方案在不同种族和族裔群体内部和之间存在差异，但现有的行政数据库缺乏反映重要社会文化差异的粒度。我们衡量了自然语言处理算法识别特定移民群体的功效。该算法在识别单一机构电子病历中的索马里患者方面表现出了准确性和精度。这项技术有望在美国的当地医疗保健环境中识别和跟踪移民和难民。
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本文引用的文献
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2
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