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RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning

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  • Ji-Sung Kim
  • Xin Gao
  • Andrey Rzhetsky

Abstract

Anonymized electronic medical records are an increasingly popular source of research data. However, these datasets often lack race and ethnicity information. This creates problems for researchers modeling human disease, as race and ethnicity are powerful confounders for many health exposures and treatment outcomes; race and ethnicity are closely linked to population-specific genetic variation. We showed that deep neural networks generate more accurate estimates for missing racial and ethnic information than competing methods (e.g., logistic regression, random forest, support vector machines, and gradient-boosted decision trees). RIDDLE yielded significantly better classification performance across all metrics that were considered: accuracy, cross-entropy loss (error), precision, recall, and area under the curve for receiver operating characteristic plots (all p

Suggested Citation

  • Ji-Sung Kim & Xin Gao & Andrey Rzhetsky, 2018. "RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-15, April.
  • Handle: RePEc:plo:pcbi00:1006106
    DOI: 10.1371/journal.pcbi.1006106
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    1. Barber, S. & Hickson, D.A. & Wang, X. & Sims, M. & Nelson, C. & Diez-Roux, A.V., 2016. "Neighborhood disadvantage, poor social conditions, and cardiovascular disease incidence among African American adults in the Jackson heart study," American Journal of Public Health, American Public Health Association, vol. 106(12), pages 2219-2226.
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    Cited by:

    1. Benjamin Lu & Jia Wan & Derek Ouyang & Jacob Goldin & Daniel E. Ho, 2024. "Quantifying the Uncertainty of Imputed Demographic Disparity Estimates: The Dual Bootstrap," NBER Chapters, in: Race, Ethnicity, and Economic Statistics for the 21st Century, National Bureau of Economic Research, Inc.

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