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The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects.

, , , , and . Int. J. Medical Informatics, 82 (4): 239-247 (2013)

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Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study., , , , , , , , , and 10 other author(s). J. Am. Medical Informatics Assoc., 19 (2): 212-218 (2012)Design and implementation of a privacy preserving electronic health record linkage tool in Chicago., , , , , , , , , and 7 other author(s). J. Am. Medical Informatics Assoc., 22 (5): 1072-1080 (2015)A Novel Patient Recruitment Strategy: Patient Selection Directly from the Community through Linkage to Clinical Data., , , , , , , , , and 6 other author(s). Appl. Clin. Inform., 09 (01): 114-121 (2018)Data Collection Methods to Support a Multi-State Practice Care Transformation Network., , , , , , and . CRI, AMIA, (2016)Iterative Design and Modeling for the PCORnet Common Data Model v3.0., , , , , , and . CRI, AMIA, (2016)Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus., , , , , , , , and . JAMIA, 19 (2): 219-224 (2012)SMCQL: Secure Query Processing for Private Data Networks., , , , , and . Proc. VLDB Endow., 10 (6): 673-684 (2017)VaultDB: A Real-World Pilot of Secure Multi-Party Computation within a Clinical Research Network., , , , , , , , , and 7 other author(s). CoRR, (2022)Mining the Best Observational Window to Model Social Phenomena., , , , , , , , and . CIC, page 46-55. IEEE Computer Society, (2018)Privacy-Preserving Record Linkage to Identify Fragmented Electronic Medical Records in the All of Us Research Program., , , , , , , , , and 2 other author(s). PKDD/ECML Workshops (2), volume 1168 of Communications in Computer and Information Science, page 79-87. Springer, (2019)