Literature DB >> 16779108

Abbreviation and acronym disambiguation in clinical discourse.

Sergeui Pakhomov1, Ted Pedersen, Christopher G Chute.   

Abstract

Use of abbreviations and acronyms is pervasive in clinical reports despite many efforts to limit the use of ambiguous and unsanctioned abbreviations and acronyms. Due to the fact that many abbreviations and acronyms are ambiguous with respect to their sense, complete and accurate text analysis is impossible without identification of the sense that was intended for a given abbreviation or acronym. We present the results of an experiment where we used the contexts harvested from the Internet through Google API to collect contextual data for a set of 8 acronyms found in clinical notes at the Mayo Clinic. We then used the contexts to disambiguate the sense of abbreviations in a manually annotated corpus.

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Year:  2005        PMID: 16779108      PMCID: PMC1560669     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  4 in total

1.  A study of abbreviations in the UMLS.

Authors:  H Liu; Y A Lussier; C Friedman
Journal:  Proc AMIA Symp       Date:  2001

2.  Evaluating the UMLS as a source of lexical knowledge for medical language processing.

Authors:  C Friedman; H Liu; L Shagina; S Johnson; G Hripcsak
Journal:  Proc AMIA Symp       Date:  2001

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

Authors:  Hongfang Liu; Stephen B Johnson; Carol Friedman
Journal:  J Am Med Inform Assoc       Date:  2002 Nov-Dec       Impact factor: 4.497

4.  A study of abbreviations in MEDLINE abstracts.

Authors:  Hongfang Liu; Alan R Aronson; Carol Friedman
Journal:  Proc AMIA Symp       Date:  2002
  4 in total
  20 in total

1.  Tailoring vocabularies for NLP in sub-domains: a method to detect unused word sense.

Authors:  Rosa L Figueroa; Qing Zeng-Treitler; Sergey Goryachev; Eduardo P Wiechmann
Journal:  AMIA Annu Symp Proc       Date:  2009-11-14

2.  A comparative study of supervised learning as applied to acronym expansion in clinical reports.

Authors:  Mahesh Joshi; Serguei Pakhomov; Ted Pedersen; Christopher G Chute
Journal:  AMIA Annu Symp Proc       Date:  2006

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

Authors:  Sungrim Moon; Serguei Pakhomov; Nathan Liu; James O Ryan; Genevieve B Melton
Journal:  J Am Med Inform Assoc       Date:  2013-06-27       Impact factor: 4.497

4.  A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time.

Authors:  Y Wu; J C Denny; S T Rosenbloom; R A Miller; D A Giuse; M Song; H Xu
Journal:  Appl Clin Inform       Date:  2015-06-03       Impact factor: 2.342

5.  Clinical Word Sense Disambiguation with Interactive Search and Classification.

Authors:  Yue Wang; Kai Zheng; Hua Xu; Qiaozhu Mei
Journal:  AMIA Annu Symp Proc       Date:  2017-02-10

6.  Towards Comprehensive Clinical Abbreviation Disambiguation Using Machine-Labeled Training Data.

Authors:  Gregory P Finley; Serguei V S Pakhomov; Reed McEwan; Genevieve B Melton
Journal:  AMIA Annu Symp Proc       Date:  2017-02-10

7.  Distinction between medical and non-medical usages of short forms in clinical narratives.

Authors:  Sungrim Moon; Donna Ihrke; Yuqun Zeng; Hongfang Liu
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

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

Authors:  Sungrim Moon; Serguei Pakhomov; Genevieve B Melton
Journal:  AMIA Annu Symp Proc       Date:  2012-11-03

9.  Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept Embeddings

Authors:  Akm Sabbir; Antonio Jimeno-Yepes; Ramakanth Kavuluru
Journal:  Proc IEEE Int Symp Bioinformatics Bioeng       Date:  2018-01-11

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

Authors:  Naoaki Okazaki; Sophia Ananiadou; Jun'ichi Tsujii
Journal:  Bioinformatics       Date:  2010-03-25       Impact factor: 6.937

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