Work

Tracking Health Disparities Through Natural-Language Processing

Year: 2013

Type: article

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.

Source: American Journal of Public Health

DOI: 10.2105/ajph.2012.300943

Authors: Mark L. Wieland, Stephen T. Wu, Vinod C. Kaggal, Barbara P. Yawn

Institutions: Mayo Clinic, Olmsted Medical Center

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Reduced inequalities, Good health and well-being

Open Access status: green

Funders: U.S. Department of Health and Human Services, Mayo Clinic, Center for Individualized Medicine, Mayo Clinic, National Center for Advancing Translational Sciences