South B - Search Results

1

Characterizing VA Users with the OMOP Common Data Model.

Viernes B, Lynch KE, South B, Coronado G, DuVall SL. Viernes B, et al. Among authors: south b. Stud Health Technol Inform. 2019 Aug 21;264:1614-1615. doi: 10.3233/SHTI190561. Stud Health Technol Inform. 2019. PMID: 31438258

2

A Tale of Two Databases: The DoD and VA Infrastructure for Clinical Intelligence (DaVINCI).

DuVall SL, Matheny ME, Ibragimov IR, Oats TD, Tucker JN, South BR, Turano A, Saoudian H, Kangas C, Hofmann K, Funk W, Nichols C, Bonnema A, Ferrucci L, Nebeker JR. DuVall SL, et al. Among authors: south br. Stud Health Technol Inform. 2019 Aug 21;264:1660-1661. doi: 10.3233/SHTI190584. Stud Health Technol Inform. 2019. PMID: 31438280

3

Qualitative analysis of workflow modifications used to generate the reference standard for the 2010 i2b2/VA challenge.

South BR, Shen S, Barrus R, DuVall SL, Uzuner O, Weir C. South BR, et al. AMIA Annu Symp Proc. 2011;2011:1243-51. Epub 2011 Oct 22. AMIA Annu Symp Proc. 2011. PMID: 22195185 Free PMC article.

4

Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure.

Garvin JH, DuVall SL, South BR, Bray BE, Bolton D, Heavirland J, Pickard S, Heidenreich P, Shen S, Weir C, Samore M, Goldstein MK. Garvin JH, et al. J Am Med Inform Assoc. 2012 Sep-Oct;19(5):859-66. doi: 10.1136/amiajnl-2011-000535. Epub 2012 Mar 21. J Am Med Inform Assoc. 2012. PMID: 22437073 Free PMC article.

5

Using natural language processing on the free text of clinical documents to screen for evidence of homelessness among US veterans.

Gundlapalli AV, Carter ME, Palmer M, Ginter T, Redd A, Pickard S, Shen S, South B, Divita G, Duvall S, Nguyen TM, D'Avolio LW, Samore M. Gundlapalli AV, et al. Among authors: south b. AMIA Annu Symp Proc. 2013 Nov 16;2013:537-46. eCollection 2013. AMIA Annu Symp Proc. 2013. PMID: 24551356 Free PMC article.

6

2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text.

Uzuner Ö, South BR, Shen S, DuVall SL. Uzuner Ö, et al. J Am Med Inform Assoc. 2011 Sep-Oct;18(5):552-6. doi: 10.1136/amiajnl-2011-000203. Epub 2011 Jun 16. J Am Med Inform Assoc. 2011. PMID: 21685143 Free PMC article.

7

What a catch! traits that define good annotators.

Forbush TB, Shen S, South BR, Duvalla SL. Forbush TB, et al. Among authors: south br. Stud Health Technol Inform. 2013;192:1213. Stud Health Technol Inform. 2013. PMID: 23920987

8

Text de-identification for privacy protection: a study of its impact on clinical text information content.

Meystre SM, Ferrández Ó, Friedlin FJ, South BR, Shen S, Samore MH. Meystre SM, et al. J Biomed Inform. 2014 Aug;50:142-50. doi: 10.1016/j.jbi.2014.01.011. Epub 2014 Feb 3. J Biomed Inform. 2014. PMID: 24502938 Free article.

9

The use of natural language processing on narrative medication schedules to compute average weekly dose.

Lu CC, Leng J, Cannon GW, Zhou X, Egger M, South B, Burningham Z, Zeng Q, Sauer BC. Lu CC, et al. Among authors: south b. Pharmacoepidemiol Drug Saf. 2016 Dec;25(12):1414-1424. doi: 10.1002/pds.4086. Epub 2016 Sep 16. Pharmacoepidemiol Drug Saf. 2016. PMID: 27633139

10

Automatically detecting medications and the reason for their prescription in clinical narrative text documents.

Meystre SM, Thibault J, Shen S, Hurdle JF, South BR. Meystre SM, et al. Stud Health Technol Inform. 2010;160(Pt 2):944-8. Stud Health Technol Inform. 2010. PMID: 20841823 Free PMC article.

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