Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study

By Abel N. Kho, M. Hayes, L. Rasmussen-Torvik, J. Pacheco, William K. Thompson, Loren L. Armstrong, Joshua Charles Denny, P. Peissig, Aaron W. Miller, Wei-Qi Wei and 10 more. 2012

287 citations recorded by the source corpus.

Subfields: Endocrinology, Diabetes and Metabolism, Genetics

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In Endocrinology, Diabetes and Metabolism

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Citations in the corpus

RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism (2016, methodology): “via decision trees [27]), 2) case-based reasoning by finding similar patients (e.”
A machine learning-based framework to identify type 2 diabetes through electronic health records (2016, methodology): “As a comparison, the state-of-the-art expert algorithm [11] leads to performance of 0.84 in accuracy, 0.78 in sensitivity, 1.00 in specificity, and 0.71 in AUC.”
Discriminative and Distinct Phenotyping by Constrained Tensor Factorization (2017, unknown): “  f y y A C B C B ( ) 2( ) ( ) 1 2 exp( ) exp( ) (5) i T”
Identification of type 2 diabetes subgroups through topological analysis of patient similarity (2015, methodology): “T2D and non-T2D control phenotypes were defined by the eMERGE phenotyping algorithm ( 16 , 17 ).”
Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data (2013, unknown): “3 and 8 ).”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, methodology): “created, validated and implemented throughout the network, and the results of genetic replications have been published [36,56-58].”
Genome- and Phenome-Wide Analysis of Cardiac Conduction Identifies Markers of Arrhythmia Risk (2013, background): “One of the key lessons in the eMERGE experience to date has been that algorithms to identify cases and controls for genomic or other study can be successfully deployed across multiple EMR systems.(16,42,43) Thus, as the number of subjects with dense genomic information across multiple EMR systems gr”
Defining and measuring completeness of electronic health records for secondary use (2013, unknown): “Some of the phenotyping algorithms developed by the eMERGE Network, for example, rely upon the presence of multiple instances of the same laboratory tests, diagnoses, or medications [31], and sometimes specify desired time periods between the recording of these data within the EHR [32,33].”