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Items: 1 to 20 of 62

1.

An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records.

Schildcrout JS, Basford MA, Pulley JM, Masys DR, Roden DM, Wang D, Chute CG, Kullo IJ, Carrell D, Peissig P, Kho A, Denny JC.

J Biomed Inform. 2010 Dec;43(6):914-23. doi: 10.1016/j.jbi.2010.07.011. Epub 2010 Aug 3.

2.

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.

3.

Baseline characteristic differences between patients prescribed sitagliptin vs. other oral antihyperglycemic agents: analysis of a US electronic medical record database.

Zhang Q, Rajagopalan S, Mavros P, Engel SS, Davies MJ, Yin D, Radican L.

Curr Med Res Opin. 2010 Jul;26(7):1697-703. doi: 10.1185/03007995.2010.489029.

PMID:
20465367
4.

Concordance between administrative health data and medical records for diabetes status in coronary heart disease patients: a retrospective linked data study.

Nedkoff L, Knuiman M, Hung J, Sanfilippo FM, Katzenellenbogen JM, Briffa TG.

BMC Med Res Methodol. 2013 Oct 1;13:121. doi: 10.1186/1471-2288-13-121.

5.

A comparison of phenotype definitions for diabetes mellitus.

Richesson RL, Rusincovitch SA, Wixted D, Batch BC, Feinglos MN, Miranda ML, Hammond WE, Califf RM, Spratt SE.

J Am Med Inform Assoc. 2013 Dec;20(e2):e319-26. doi: 10.1136/amiajnl-2013-001952. Epub 2013 Sep 11.

6.

Prevalence of peripheral arterial disease in type 2 diabetes mellitus and its correlation with coronary artery disease and its risk factors.

Agarwal AK, Singh M, Arya V, Garg U, Singh VP, Jain V.

J Assoc Physicians India. 2012 Jul;60:28-32.

PMID:
23405538
7.

Validation of pediatric diabetes case identification approaches for diagnosed cases by using information in the electronic health records of a large integrated managed health care organization.

Lawrence JM, Black MH, Zhang JL, Slezak JM, Takhar HS, Koebnick C, Mayer-Davis EJ, Zhong VW, Dabelea D, Hamman RF, Reynolds K.

Am J Epidemiol. 2014 Jan 1;179(1):27-38. doi: 10.1093/aje/kwt230. Epub 2013 Oct 7.

PMID:
24100956
8.

Using electronic patient records to discover disease correlations and stratify patient cohorts.

Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T, Søeby K, Bredkjær S, Juul A, Werge T, Jensen LJ, Brunak S.

PLoS Comput Biol. 2011 Aug;7(8):e1002141. doi: 10.1371/journal.pcbi.1002141. Epub 2011 Aug 25.

9.

Cataract research using electronic health records.

Waudby CJ, Berg RL, Linneman JG, Rasmussen LV, Peissig PL, Chen L, McCarty CA.

BMC Ophthalmol. 2011 Nov 11;11:32. doi: 10.1186/1471-2415-11-32.

10.

Prevalence, incidence and progression of peripheral arterial disease in Asian Indian type 2 diabetic patients.

Eshcol J, Jebarani S, Anjana RM, Mohan V, Pradeepa R.

J Diabetes Complications. 2014 Sep-Oct;28(5):627-31. doi: 10.1016/j.jdiacomp.2014.04.013. Epub 2014 May 2.

PMID:
24930714
11.

Prevalence of obesity, type II diabetes mellitus, hyperlipidemia, and hypertension in the United States: findings from the GE Centricity Electronic Medical Record database.

Crawford AG, Cote C, Couto J, Daskiran M, Gunnarsson C, Haas K, Haas S, Nigam SC, Schuette R.

Popul Health Manag. 2010 Jun;13(3):151-61. doi: 10.1089/pop.2009.0039.

PMID:
20521902
12.

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. eCollection 2014.

13.

Amputations and foot ulcers in patients newly diagnosed with type 2 diabetes mellitus and observed for 19 years. The role of age, gender and co-morbidity.

Bruun C, Siersma V, Guassora AD, Holstein P, de Fine Olivarius N.

Diabet Med. 2013 Aug;30(8):964-72. doi: 10.1111/dme.12196. Epub 2013 Apr 26.

PMID:
23617411
14.

LDL-C goal attainment among patients newly diagnosed with coronary heart disease or diabetes in a commercial HMO.

Nag SS, Daniel GW, Bullano MF, Kamal-Bahl S, Sajjan SG, Hu H, Alexander C.

J Manag Care Pharm. 2007 Oct;13(8):652-63.

15.

Economic burden in direct costs of concomitant chronic obstructive pulmonary disease and asthma in a Medicare Advantage population.

Blanchette CM, Gutierrez B, Ory C, Chang E, Akazawa M.

J Manag Care Pharm. 2008 Mar;14(2):176-85.

16.

Prevalence of grade II and III obesity among patients hospitalized with cardiovascular diagnoses in 2002 v. 2009.

Patil H, Astik G, House JA, O'Keefe JH, Main ML.

Mo Med. 2012 Sep-Oct;109(5):397-401.

PMID:
23097947
17.

Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications.

Warner JL, Zollanvari A, Ding Q, Zhang P, Snyder GM, Alterovitz G.

J Am Med Inform Assoc. 2013 Dec;20(e2):e281-7. doi: 10.1136/amiajnl-2013-001861. Epub 2013 Aug 1.

18.

Distinguishing incident and prevalent diabetes in an electronic medical records database.

Mamtani R, Haynes K, Finkelman BS, Scott FI, Lewis JD.

Pharmacoepidemiol Drug Saf. 2014 Feb;23(2):111-8. doi: 10.1002/pds.3557. Epub 2013 Dec 19.

19.

Accuracy of phenotyping chronic rhinosinusitis in the electronic health record.

Hsu J, Pacheco JA, Stevens WW, Smith ME, Avila PC.

Am J Rhinol Allergy. 2014 Mar-Apr;28(2):140-4. doi: 10.2500/ajra.2014.28.4012.

PMID:
24717952
20.

What you can do now to prepare for ICD-10.

Kuppe P.

Minn Med. 2011 Apr;94(4):37-9.

PMID:
21560881

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