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

Ji-Sung Kim, Xin Gao, Andrey Rzhetsky

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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 < 10-9). We made specific efforts to interpret the trained neural network models to identify, quantify, and visualize medical features which are predictive of race and ethnicity. We used these characterizations of informative features to perform a systematic comparison of differential disease patterns by race and ethnicity. The fact that clinical histories are informative for imputing race and ethnicity could reflect (1) a skewed distribution of blue- and white-collar professions across racial and ethnic groups, (2) uneven accessibility and subjective importance of prophylactic health, (3) possible variation in lifestyle, such as dietary habits, and (4) differences in background genetic variation which predispose to diseases.
Original languageEnglish (US)
Pages (from-to)e1006106
JournalPLOS Computational Biology
Issue number4
StatePublished - Apr 26 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): FCC/1/1976-04, URF/1/3007-01, URF/1/3450-01, URF/1/3454-01
Acknowledgements: The study was supported by funds from the Defense Advanced Projects Agency, contract W911NF1410333 to AR, https://urldefense.proofpoint.com/v2/url?u=http-3A__www.darpa.mil_program_big-2Dmechanism&amp;d=DwIGaQ&amp;c=Nd1gv_ZWYNIRyZYZmXb18oVfc3lTqv2smA_esABG70U&amp;r=ULvNIgo15mH_8cCBmiM1KBF_qHRW8ZMYO-_ZDPm3uOp9kFqARW63OFcx12Y06DIX&amp;m=t94GIr1nrxziPIHpUNauHQejNovIkVRHPMsNYXkgjNg&amp;s=L72IDKoNZ_89dilSQYA3xsw98W2QbGXv0RdpZxi0oQk&amp;e=, the National Heart Lung and Blood Institute, award R01HL122712 to A.R., https://urldefense.proofpoint.com/v2/url?u=https-3A__www.nhlbi.nih.gov&amp;d=DwIGaQ&amp;c=Nd1gv_ZWYNIRyZYZmXb18oVfc3lTqv2smA_esABG70U&amp;r=ULvNIgo15mH_8cCBmiM1KBF_qHRW8ZMYO-_ZDPm3uOp9kFqARW63OFcx12Y06DIX&amp;m=t94GIr1nrxziPIHpUNauHQejNovIkVRHPMsNYXkgjNg&amp;s=PSG7vwNtEqDmJ-ch-N921YI8xACd-N-EyAJZbHII6Fw&amp;e=, the National Institute of Mental Health, award P50 MH094267 to AR, https://urldefense.proofpoint.com/v2/url?u=https-3A__grants.nih.gov_grants_guide_pa-2Dfiles_PAR-2D14-2D120.html&amp;d=DwIGaQ&amp;c=Nd1gv_ZWYNIRyZYZmXb18oVfc3lTqv2smA_esABG70U&amp;r=ULvNIgo15mH_8cCBmiM1KBF_qHRW8ZMYO-_ZDPm3uOp9kFqARW63OFcx12Y06DIX&amp;m=t94GIr1nrxziPIHpUNauHQejNovIkVRHPMsNYXkgjNg&amp;s=Qu6HXGbIRPOE-yJSf3SFxJxutavu5K_Ic3FkHjau-s0&amp;e=, by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR), awards FCC/1/1976-04, URF/1/3007-01, URF/1/3450-01 and URF/1/3454-01to XG, and a gift from Liz and Kent Dauten to AR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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