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Items: 1 to 20 of 177

1.

Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.

Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Choudhary V, Basford M, Chute CG, Kullo IJ, Li R, Pacheco JA, Rasmussen LV, Spangler L, Denny JC.

J Am Med Inform Assoc. 2013 Jun;20(e1):e147-54. doi: 10.1136/amiajnl-2012-000896.

2.

Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality.

Zuvich RL, Armstrong LL, Bielinski SJ, Bradford Y, Carlson CS, Crawford DC, Crenshaw AT, de Andrade M, Doheny KF, Haines JL, Hayes MG, Jarvik GP, Jiang L, Kullo IJ, Li R, Ling H, Manolio TA, Matsumoto ME, McCarty CA, McDavid AN, Mirel DB, Olson LM, Paschall JE, Pugh EW, Rasmussen LV, Rasmussen-Torvik LJ, Turner SD, Wilke RA, Ritchie MD.

Genet Epidemiol. 2011 Dec;35(8):887-98. doi: 10.1002/gepi.20639.

3.

Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records.

Sinnott JA, Dai W, Liao KP, Shaw SY, Ananthakrishnan AN, Gainer VS, Karlson EW, Churchill S, Szolovits P, Murphy S, Kohane I, Plenge R, Cai T.

Hum Genet. 2014 Nov;133(11):1369-82. doi: 10.1007/s00439-014-1466-9.

4.

Development and validation of an electronic phenotyping algorithm for chronic kidney disease.

Nadkarni GN, Gottesman O, Linneman JG, Chase H, Berg RL, Farouk S, Nadukuru R, Lotay V, Ellis S, Hripcsak G, Peissig P, Weng C, Bottinger EP.

AMIA Annu Symp Proc. 2014 Nov 14;2014:907-16.

5.

A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury.

Overby CL, Pathak J, Gottesman O, Haerian K, Perotte A, Murphy S, Bruce K, Johnson S, Talwalkar J, Shen Y, Ellis S, Kullo I, Chute C, Friedman C, Bottinger E, Hripcsak G, Weng C.

J Am Med Inform Assoc. 2013 Dec;20(e2):e243-52. doi: 10.1136/amiajnl-2013-001930.

6.

A Robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network.

Bielinski SJ, Pathak J, Carrell DS, Takahashi PY, Olson JE, Larson NB, Liu H, Sohn S, Wells QS, Denny JC, Rasmussen-Torvik LJ, Pacheco JA, Jackson KL, Lesnick TG, Gullerud RE, Decker PA, Pereira NL, Ryu E, Dart RA, Peissig P, Linneman JG, Jarvik GP, Larson EB, Bock JA, Tromp GC, de Andrade M, Roger VL.

J Cardiovasc Transl Res. 2015 Nov;8(8):475-83. doi: 10.1007/s12265-015-9644-2.

7.
8.

Design patterns for the development of electronic health record-driven phenotype extraction algorithms.

Rasmussen LV, Thompson WK, Pacheco JA, Kho AN, Carrell DS, Pathak J, Peissig PL, Tromp G, Denny JC, Starren JB.

J Biomed Inform. 2014 Oct;51:280-6. doi: 10.1016/j.jbi.2014.06.007.

9.

From patient care to research: a validation study examining the factors contributing to data quality in a primary care electronic medical record database.

Coleman N, Halas G, Peeler W, Casaclang N, Williamson T, Katz A.

BMC Fam Pract. 2015 Feb 5;16:11. doi: 10.1186/s12875-015-0223-z.

10.

The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future.

Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R, Manolio TA, Sanderson SC, Kannry J, Zinberg R, Basford MA, Brilliant M, Carey DJ, Chisholm RL, Chute CG, Connolly JJ, Crosslin D, Denny JC, Gallego CJ, Haines JL, Hakonarson H, Harley J, Jarvik GP, Kohane I, Kullo IJ, Larson EB, McCarty C, Ritchie MD, Roden DM, Smith ME, Böttinger EP, Williams MS; eMERGE Network..

Genet Med. 2013 Oct;15(10):761-71. doi: 10.1038/gim.2013.72. Review.

11.

The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies.

McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB, Li R, Masys DR, Ritchie MD, Roden DM, Struewing JP, Wolf WA; eMERGE Team..

BMC Med Genomics. 2011 Jan 26;4:13. doi: 10.1186/1755-8794-4-13.

12.

Electronic medical records for genetic research: results of the eMERGE consortium.

Kho AN, Pacheco JA, Peissig PL, Rasmussen L, Newton KM, Weston N, Crane PK, Pathak J, Chute CG, Bielinski SJ, Kullo IJ, Li R, Manolio TA, Chisholm RL, Denny JC.

Sci Transl Med. 2011 Apr 20;3(79):79re1. doi: 10.1126/scitranslmed.3001807.

13.

The effects of electronic medical record phenotyping details on genetic association studies: HDL-C as a case study.

Dumitrescu L, Goodloe R, Bradford Y, Farber-Eger E, Boston J, Crawford DC.

BioData Min. 2015 May 6;8:15. doi: 10.1186/s13040-015-0048-2.

14.

A design of experiments approach to validation sampling for logistic regression modeling with error-prone medical records.

Ouyang L, Apley DW, Mehrotra S.

J Am Med Inform Assoc. 2016 Apr;23(e1):e71-8. doi: 10.1093/jamia/ocv132.

PMID:
26374705
15.

ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow.

Borthwick KM, Smelser DT, Bock JA, Elmore JR, Ryer EJ, Ye Z, Pacheco JA, Carrell DS, Michalkiewicz M, Thompson WK, Pathak J, Bielinski SJ, Denny JC, Linneman JG, Peissig PL, Kho AN, Gottesman O, Parmar H, Kullo IJ, McCarty CA, Böttinger EP, Larson EB, Jarvik GP, Harley JB, Bajwa T, Franklin DP, Carey DJ, Kuivaniemi H, Tromp G.

Int J Biomed Data Min. 2015 Dec;4(1). pii: 113.

16.

Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts.

Liao KP, Ananthakrishnan AN, Kumar V, Xia Z, Cagan A, Gainer VS, Goryachev S, Chen P, Savova GK, Agniel D, Churchill S, Lee J, Murphy SN, Plenge RM, Szolovits P, Kohane I, Shaw SY, Karlson EW, Cai T.

PLoS One. 2015 Aug 24;10(8):e0136651. doi: 10.1371/journal.pone.0136651.

17.

Retrospective derivation and validation of a search algorithm to identify extubation failure in the intensive care unit.

Rishi MA, Kashyap R, Wilson G, Hocker S.

BMC Anesthesiol. 2014 May 23;14:41. doi: 10.1186/1471-2253-14-41.

18.

Retrospective derivation and validation of a search algorithm to identify emergent endotracheal intubations in the intensive care unit.

Smischney NJ, Velagapudi VM, Onigkeit JA, Pickering BW, Herasevich V, Kashyap R.

Appl Clin Inform. 2013 Sep 4;4(3):419-27. doi: 10.4338/ACI-2013-05-RA-0033.

19.

Importance of multi-modal approaches to effectively identify cataract cases from electronic health records.

Peissig PL, Rasmussen LV, Berg RL, Linneman JG, McCarty CA, Waudby C, Chen L, Denny JC, Wilke RA, Pathak J, Carrell D, Kho AN, Starren JB.

J Am Med Inform Assoc. 2012 Mar-Apr;19(2):225-34. doi: 10.1136/amiajnl-2011-000456.

20.

Derivation and validation of a search algorithm to retrospectively identify mechanical ventilation initiation in the intensive care unit.

Smischney NJ, Velagapudi VM, Onigkeit JA, Pickering BW, Herasevich V, Kashyap R.

BMC Med Inform Decis Mak. 2014 Jun 25;14:55. doi: 10.1186/1472-6947-14-55.

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