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Items: 1 to 20 of 283

1.

Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.

Pathak J, Bailey KR, Beebe CE, Bethard S, Carrell DC, Chen PJ, Dligach D, Endle CM, Hart LA, Haug PJ, Huff SM, Kaggal VC, Li D, Liu H, Marchant K, Masanz J, Miller T, Oniki TA, Palmer M, Peterson KJ, Rea S, Savova GK, Stancl CR, Sohn S, Solbrig HR, Suesse DB, Tao C, Taylor DP, Westberg L, Wu S, Zhuo N, Chute CG.

J Am Med Inform Assoc. 2013 Dec;20(e2):e341-8. doi: 10.1136/amiajnl-2013-001939. Epub 2013 Nov 4.

2.

Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project.

Rea S, Pathak J, Savova G, Oniki TA, Westberg L, Beebe CE, Tao C, Parker CG, Haug PJ, Huff SM, Chute CG.

J Biomed Inform. 2012 Aug;45(4):763-71. doi: 10.1016/j.jbi.2012.01.009. Epub 2012 Feb 4.

3.

Clinical element models in the SHARPn consortium.

Oniki TA, Zhuo N, Beebe CE, Liu H, Coyle JF, Parker CG, Solbrig HR, Marchant K, Kaggal VC, Chute CG, Huff SM.

J Am Med Inform Assoc. 2016 Mar;23(2):248-56. doi: 10.1093/jamia/ocv134. Epub 2015 Nov 13.

PMID:
26568604
4.

Modeling and executing electronic health records driven phenotyping algorithms using the NQF Quality Data Model and JBoss® Drools Engine.

Li D, Endle CM, Murthy S, Stancl C, Suesse D, Sottara D, Huff SM, Chute CG, Pathak J.

AMIA Annu Symp Proc. 2012;2012:532-41. Epub 2012 Nov 3.

5.

An evaluation of the NQF Quality Data Model for representing Electronic Health Record driven phenotyping algorithms.

Thompson WK, Rasmussen LV, Pacheco JA, Peissig PL, Denny JC, Kho AN, Miller A, Pathak J.

AMIA Annu Symp Proc. 2012;2012:911-20. Epub 2012 Nov 3.

6.
7.

Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts.

Fernández-Breis JT, Maldonado JA, Marcos M, Legaz-García Mdel C, Moner D, Torres-Sospedra J, Esteban-Gil A, Martínez-Salvador B, Robles M.

J Am Med Inform Assoc. 2013 Dec;20(e2):e288-96. doi: 10.1136/amiajnl-2013-001923. Epub 2013 Aug 9.

8.

The SHARPn project on secondary use of Electronic Medical Record data: progress, plans, and possibilities.

Chute CG, Pathak J, Savova GK, Bailey KR, Schor MI, Hart LA, Beebe CE, Huff SM.

AMIA Annu Symp Proc. 2011;2011:248-56. Epub 2011 Oct 22.

9.

CER Hub: An informatics platform for conducting comparative effectiveness research using multi-institutional, heterogeneous, electronic clinical data.

Hazlehurst BL, Kurtz SE, Masica A, Stevens VJ, McBurnie MA, Puro JE, Vijayadeva V, Au DH, Brannon ED, Sittig DF.

Int J Med Inform. 2015 Oct;84(10):763-73. doi: 10.1016/j.ijmedinf.2015.06.002. Epub 2015 Jun 10.

PMID:
26138036
10.

A Standards-based Semantic Metadata Repository to Support EHR-driven Phenotype Authoring and Execution.

Jiang G, Solbrig HR, Kiefer R, Rasmussen LV, Mo H, Speltz P, Thompson WK, Denny JC, Chute CG, Pathak J.

Stud Health Technol Inform. 2015;216:1098.

11.

A numerical similarity approach for using retired Current Procedural Terminology (CPT) codes for electronic phenotyping in the Scalable Collaborative Infrastructure for a Learning Health System (SCILHS).

Klann JG, Phillips LC, Turchin A, Weiler S, Mandl KD, Murphy SN.

BMC Med Inform Decis Mak. 2015 Dec 11;15:104. doi: 10.1186/s12911-015-0223-x.

12.

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.

Yu S, Liao KP, Shaw SY, Gainer VS, Churchill SE, Szolovits P, Murphy SN, Kohane IS, Cai T.

J Am Med Inform Assoc. 2015 Sep;22(5):993-1000. doi: 10.1093/jamia/ocv034. Epub 2015 Apr 29.

13.

Natural Language Processing Technologies in Radiology Research and Clinical Applications.

Cai T, Giannopoulos AA, Yu S, Kelil T, Ripley B, Kumamaru KK, Rybicki FJ, Mitsouras D.

Radiographics. 2016 Jan-Feb;36(1):176-91. doi: 10.1148/rg.2016150080. Review.

14.

Harmonization of detailed clinical models with clinical study data standards.

Jiang G, Evans J, Oniki TA, Coyle JF, Bain L, Huff SM, Kush RD, Chute CG.

Methods Inf Med. 2015;54(1):65-74. doi: 10.3414/ME13-02-0019. Epub 2014 Nov 26.

PMID:
25426730
15.
16.

Building bridges across electronic health record systems through inferred phenotypic topics.

Chen Y, Ghosh J, Bejan CA, Gunter CA, Gupta S, Kho A, Liebovitz D, Sun J, Denny J, Malin B.

J Biomed Inform. 2015 Jun;55:82-93. doi: 10.1016/j.jbi.2015.03.011. Epub 2015 Apr 1.

17.

Model-based auditability of clinical trial recruitment.

Curcin V, Lim Choi Keung SN, Danger R, Rossiter J, Zhao L, Arvanitis TN.

Stud Health Technol Inform. 2013;192:1223.

PMID:
23920997
18.

Representation of information about family relatives as structured data in electronic health records.

Zhou L, Lu Y, Vitale CJ, Mar PL, Chang F, Dhopeshwarkar N, Rocha RA.

Appl Clin Inform. 2014 Apr 9;5(2):349-67. doi: 10.4338/ACI-2013-10-RA-0080. eCollection 2014.

19.

Clinical data mining and research in the allergy office.

Dalan D.

Curr Opin Allergy Clin Immunol. 2010 Jun;10(3):171-7. doi: 10.1097/ACI.0b013e328337bce6. Review.

PMID:
20179584
20.

A review of approaches to identifying patient phenotype cohorts using electronic health records.

Shivade C, Raghavan P, Fosler-Lussier E, Embi PJ, Elhadad N, Johnson SB, Lai AM.

J Am Med Inform Assoc. 2014 Mar-Apr;21(2):221-30. doi: 10.1136/amiajnl-2013-001935. Epub 2013 Nov 7. Review.

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