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Items: 1 to 20 of 84

1.

PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies.

Sinnott JA, Cai F, Yu S, Hejblum BP, Hong C, Kohane IS, Liao KP.

J Am Med Inform Assoc. 2018 Oct 1;25(10):1359-1365. doi: 10.1093/jamia/ocy056.

2.

INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIES.

Verma A, Leader JB, Verma SS, Frase A, Wallace J, Dudek S, Lavage DR, Van Hout CV, Dewey FE, Penn J, Lopez A, Overton JD, Carey DJ, Ledbetter DH, Kirchner HL, Ritchie MD, Pendergrass SA.

Pac Symp Biocomput. 2016;21:168-79.

3.

Automated Feature Selection of Predictors in Electronic Medical Records Data.

Gronsbell J, Minnier J, Yu S, Liao K, Cai T.

Biometrics. 2018 Oct 24. doi: 10.1111/biom.12987. [Epub ahead of print]

PMID:
30353541
4.

Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record.

Wei WQ, Bastarache LA, Carroll RJ, Marlo JE, Osterman TJ, Gamazon ER, Cox NJ, Roden DM, Denny JC.

PLoS One. 2017 Jul 7;12(7):e0175508. doi: 10.1371/journal.pone.0175508. eCollection 2017.

5.

Relational machine learning for electronic health record-driven phenotyping.

Peissig PL, Santos Costa V, Caldwell MD, Rottscheit C, Berg RL, Mendonca EA, Page D.

J Biomed Inform. 2014 Dec;52:260-70. doi: 10.1016/j.jbi.2014.07.007. Epub 2014 Jul 15.

6.

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.

7.

Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance.

Wei WQ, Teixeira PL, Mo H, Cronin RM, Warner JL, Denny JC.

J Am Med Inform Assoc. 2016 Apr;23(e1):e20-7. doi: 10.1093/jamia/ocv130. Epub 2015 Sep 2.

8.

IDENTIFYING GENETIC ASSOCIATIONS WITH VARIABILITY IN METABOLIC HEALTH AND BLOOD COUNT LABORATORY VALUES: DIVING INTO THE QUANTITATIVE TRAITS BY LEVERAGING LONGITUDINAL DATA FROM AN EHR.

Verma SS, Lucas AM, Lavage DR, Leader JB, Metpally R, Krishnamurthy S, Dewey F, Borecki I, Lopez A, Overton J, Penn J, Reid J, Pendergrass SA, Breitwieser G, Ritchie MD.

Pac Symp Biocomput. 2017;22:533-544. doi: 10.1142/9789813207813_0049.

9.

Contrasting Association Results between Existing PheWAS Phenotype Definition Methods and Five Validated Electronic Phenotypes.

Leader JB, Pendergrass SA, Verma A, Carey DJ, Hartzel DN, Ritchie MD, Kirchner HL.

AMIA Annu Symp Proc. 2015 Nov 5;2015:824-32. eCollection 2015.

10.

Learning probabilistic phenotypes from heterogeneous EHR data.

Pivovarov R, Perotte AJ, Grave E, Angiolillo J, Wiggins CH, Elhadad N.

J Biomed Inform. 2015 Dec;58:156-165. doi: 10.1016/j.jbi.2015.10.001. Epub 2015 Oct 14.

11.

The use of electronic health records for psychiatric phenotyping and genomics.

Smoller JW.

Am J Med Genet B Neuropsychiatr Genet. 2018 Oct;177(7):601-612. doi: 10.1002/ajmg.b.32548. Epub 2017 May 30. Review.

PMID:
28557243
12.

OPENING THE DOOR TO THE LARGE SCALE USE OF CLINICAL LAB MEASURES FOR ASSOCIATION TESTING: EXPLORING DIFFERENT METHODS FOR DEFINING PHENOTYPES.

Bauer CR, Lavage D, Snyder J, Leader J, Mahoney JM, Pendergrass SA.

Pac Symp Biocomput. 2017;22:356-367. doi: 10.1142/9789813207813_0034.

13.

Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study.

Anderson AE, Kerr WT, Thames A, Li T, Xiao J, Cohen MS.

J Biomed Inform. 2016 Apr;60:162-8. doi: 10.1016/j.jbi.2015.12.006. Epub 2015 Dec 17.

14.

Relationship between very low low-density lipoprotein cholesterol concentrations not due to statin therapy and risk of type 2 diabetes: A US-based cross-sectional observational study using electronic health records.

Feng Q, Wei WQ, Chung CP, Levinson RT, Sundermann AC, Mosley JD, Bastarache L, Ferguson JF, Cox NJ, Roden DM, Denny JC, Linton MF, Edwards DRV, Stein CM.

PLoS Med. 2018 Aug 28;15(8):e1002642. doi: 10.1371/journal.pmed.1002642. eCollection 2018 Aug.

15.

Comparison of the Injury Severity Score and ICD-9 diagnosis codes as predictors of outcome in injury: analysis of 44,032 patients.

Rutledge R, Hoyt DB, Eastman AB, Sise MJ, Velky T, Canty T, Wachtel T, Osler TM.

J Trauma. 1997 Mar;42(3):477-87; discussion 487-9.

PMID:
9095116
16.

Current Scope and Challenges in Phenome-Wide Association Studies.

Verma A, Ritchie MD.

Curr Epidemiol Rep. 2017 Dec;4(4):321-329. doi: 10.1007/s40471-017-0127-7. Epub 2017 Nov 2.

17.

Semi-supervised Validation of Multiple Surrogate Outcomes with Application to Electronic Medical Records Phenotyping.

Hong C, Liao KP, Cai T.

Biometrics. 2018 Sep 29. doi: 10.1111/biom.12971. [Epub ahead of print]

PMID:
30267536
18.

EHR-based phenome wide association study in pancreatic cancer.

Adamusiak T, Shimoyama M.

AMIA Jt Summits Transl Sci Proc. 2014 Apr 7;2014:9-15. eCollection 2014.

19.

Phenome-Wide Association Study to Explore Relationships between Immune System Related Genetic Loci and Complex Traits and Diseases.

Verma A, Basile AO, Bradford Y, Kuivaniemi H, Tromp G, Carey D, Gerhard GS, Crowe JE Jr, Ritchie MD, Pendergrass SA.

PLoS One. 2016 Aug 10;11(8):e0160573. doi: 10.1371/journal.pone.0160573. eCollection 2016.

20.

Assessing the Precision of ICD-10 Codes for Uveitis in 2 Electronic Health Record Systems.

Palestine AG, Merrill PT, Saleem SM, Jabs DA, Thorne JE.

JAMA Ophthalmol. 2018 Oct 1;136(10):1186-1190. doi: 10.1001/jamaophthalmol.2018.3001.

PMID:
30054618

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