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Items: 1 to 20 of 121

1.

A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources.

Moon S, Pakhomov S, Liu N, Ryan JO, Melton GB.

J Am Med Inform Assoc. 2014 Mar-Apr;21(2):299-307. doi: 10.1136/amiajnl-2012-001506. Epub 2013 Jun 27.

2.

A study of abbreviations in clinical notes.

Xu H, Stetson PD, Friedman C.

AMIA Annu Symp Proc. 2007 Oct 11:821-5.

3.

Using UMLS lexical resources to disambiguate abbreviations in clinical text.

Kim Y, Hurdle J, Meystre SM.

AMIA Annu Symp Proc. 2011;2011:715-22. Epub 2011 Oct 22.

4.

Abbreviation and acronym disambiguation in clinical discourse.

Pakhomov S, Pedersen T, Chute CG.

AMIA Annu Symp Proc. 2005:589-93.

5.

Resolving abbreviations to their senses in Medline.

Gaudan S, Kirsch H, Rebholz-Schuhmann D.

Bioinformatics. 2005 Sep 15;21(18):3658-64. Epub 2005 Jul 21.

6.

A new clustering method for detecting rare senses of abbreviations in clinical notes.

Xu H, Wu Y, Elhadad N, Stetson PD, Friedman C.

J Biomed Inform. 2012 Dec;45(6):1075-83. doi: 10.1016/j.jbi.2012.06.003. Epub 2012 Jun 25.

7.

Challenges and practical approaches with word sense disambiguation of acronyms and abbreviations in the clinical domain.

Moon S, McInnes B, Melton GB.

Healthc Inform Res. 2015 Jan;21(1):35-42. doi: 10.4258/hir.2015.21.1.35. Epub 2015 Jan 31.

8.

ADAM: another database of abbreviations in MEDLINE.

Zhou W, Torvik VI, Smalheiser NR.

Bioinformatics. 2006 Nov 15;22(22):2813-8. Epub 2006 Sep 18.

9.

Automated disambiguation of acronyms and abbreviations in clinical texts: window and training size considerations.

Moon S, Pakhomov S, Melton GB.

AMIA Annu Symp Proc. 2012;2012:1310-9. Epub 2012 Nov 3.

10.

Building a high-quality sense inventory for improved abbreviation disambiguation.

Okazaki N, Ananiadou S, Tsujii J.

Bioinformatics. 2010 May 1;26(9):1246-53. doi: 10.1093/bioinformatics/btq129. Epub 2010 Mar 25.

11.

Methods for building sense inventories of abbreviations in clinical notes.

Xu H, Stetson PD, Friedman C.

J Am Med Inform Assoc. 2009 Jan-Feb;16(1):103-8. doi: 10.1197/jamia.M2927. Epub 2008 Oct 24.

12.

Building an abbreviation dictionary using a term recognition approach.

Okazaki N, Ananiadou S.

Bioinformatics. 2006 Dec 15;22(24):3089-95. Epub 2006 Oct 18.

13.

Automatic resolution of ambiguous terms based on machine learning and conceptual relations in the UMLS.

Liu H, Johnson SB, Friedman C.

J Am Med Inform Assoc. 2002 Nov-Dec;9(6):621-36.

14.

Using MEDLINE as a knowledge source for disambiguating abbreviations and acronyms in full-text biomedical journal articles.

Yu H, Kim W, Hatzivassiloglou V, Wilbur WJ.

J Biomed Inform. 2007 Apr;40(2):150-9. Epub 2006 Jun 7.

15.

A study of abbreviations in MEDLINE abstracts.

Liu H, Aronson AR, Friedman C.

Proc AMIA Symp. 2002:464-8.

16.

Link-topic model for biomedical abbreviation disambiguation.

Kim S, Yoon J.

J Biomed Inform. 2015 Feb;53:367-80. doi: 10.1016/j.jbi.2014.12.013. Epub 2014 Dec 30.

17.

Creating an online dictionary of abbreviations from MEDLINE.

Chang JT, Schütze H, Altman RB.

J Am Med Inform Assoc. 2002 Nov-Dec;9(6):612-20.

18.

Normalizing acronyms and abbreviations to aid patient understanding of clinical texts: ShARe/CLEF eHealth Challenge 2013, Task 2.

Mowery DL, South BR, Christensen L, Leng J, Peltonen LM, Salanterä S, Suominen H, Martinez D, Velupillai S, Elhadad N, Savova G, Pradhan S, Chapman WW.

J Biomed Semantics. 2016 Jul 1;7:43. doi: 10.1186/s13326-016-0084-y.

19.

Enhancing acronym/abbreviation knowledge bases with semantic information.

Torii M, Liu H.

AMIA Annu Symp Proc. 2007 Oct 11:731-5.

20.

Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation.

Jimeno-Yepes AJ, McInnes BT, Aronson AR.

BMC Bioinformatics. 2011 Jun 2;12:223. doi: 10.1186/1471-2105-12-223.

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