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Challenges with quality of race and ethnicity data in observational databases., , , , , , , , , and . J. Am. Medical Informatics Assoc., 26 (8-9): 730-736 (2019)Big Data for Healthcare and Life Sciences: Learning Useful Insights from Imperfect Data., , , , and . AMIA, AMIA, (2016)Phenotype Concept Set Construction from Concept Pair Likelihoods., , , , , , , , , and . AMIA, AMIA, (2020)Transforming NHANES Database to the OMOP Common Data Model., , , , and . AMIA, AMIA, (2014)A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases., , , , , and . npj Digit. Medicine, (2023)Adapting electronic health records-derived phenotypes to claims data: Lessons learned in using limited clinical data for phenotyping., , , , , and . J. Biomed. Informatics, (2020)Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers., , , , , , , , , and 7 other author(s). MedInfo, volume 216 of Studies in Health Technology and Informatics, page 574-578. IOS Press, (2015)The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0., , , , , , , and . J. Am. Medical Informatics Assoc., 25 (3): 239-247 (2018)Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability., , , , and . BMC Medical Informatics Decis. Mak., 22 (1): 142 (2022)From Large-Scale Network Analytics to Clinical Solutions in OHDSI., , , and . AMIA, AMIA, (2017)