| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
Research Paper |
Affiliations of the authors: Columbia University, New York, New York.
Correspondence and reprints: Hong Yu, MS, MPhil, Department of Medical Informatics, 622 W 168th Street, Vanderbilt Clinic, 5th Floor, New York, NY 10032; e-mail: <hy52{at}columbia.edu>.
Objective: To develop methods that automatically map abbreviations to their full forms in biomedical articles.
Methods: The authors developed two methods of mapping defined and undefined abbreviations (defined abbreviations are paired with their full forms in the articles, whereas undefined ones are not). For defined abbreviations, they developed a set of pattern-matching rules to map an abbreviation to its full form and implemented the rules into a software program, AbbRE (for "abbreviation recognition and extraction"). Using the opinions of domain experts as a reference standard, they evaluated the recall and precision of AbbRE for defined abbreviations in ten biomedical articles randomly selected from the ten most frequently cited medical and biological journals. They also measured the percentage of undefined abbreviations in the same set of articles, and they investigated whether they could map undefined abbreviations to any of four public abbreviation databases (GenBank LocusLink, SWISSPROT, LRABR of the UMLS Specialist Lexicon, and BioABACUS).
Results: AbbRE had an average 0.70 recall and 0.95 precision for the defined abbreviations. The authors found that an average of 25 percent of abbreviations were defined in biomedical articles and that of a randomly selected subset of undefined abbreviations, 68 percent could be mapped to any of four abbreviation databases. They also found that many abbreviations are ambiguous (i.e., they map to more than one full form in abbreviation databases).
Conclusion: AbbRE is efficient for mapping defined abbreviations. To couple AbbRE with abbreviation databases for the mapping of undefined abbreviations, not only exhaustive abbreviation databases but also a method to resolve the ambiguity of abbreviations in the databases are needed.
This article has been cited by other articles:
 |
|
 |
|
  N. Okazaki and S. Ananiadou Building an abbreviation dictionary using a term recognition approach Bioinformatics, December 15, 2006; 22(24): 3089 - 3095. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. Zhou, V. I. Torvik, and N. R. Smalheiser ADAM: another database of abbreviations in MEDLINE Bioinformatics, November 15, 2006; 22(22): 2813 - 2818. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Y. Sun and Y. Sun A System for Automated Lexical Mapping J. Am. Med. Inform. Assoc., May 1, 2006; 13(3): 334 - 343. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Gaudan, H. Kirsch, and D. Rebholz-Schuhmann Resolving abbreviations to their senses in Medline Bioinformatics, September 15, 2005; 21(18): 3658 - 3664. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Ao and T. Takagi ALICE: An Algorithm to Extract Abbreviations from MEDLINE J. Am. Med. Inform. Assoc., September 1, 2005; 12(5): 576 - 586. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. D. Wren, J. T. Chang, J. Pustejovsky, E. Adar, H. R. Garner, and R. B. Altman Biomedical term mapping databases Nucleic Acids Res., January 1, 2005; 33(suppl_1): D289 - D293. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. C. Denny, J. D. Smithers, R. A. Miller, and A. Spickard III "Understanding" Medical School Curriculum Content Using KnowledgeMap J. Am. Med. Inform. Assoc., July 1, 2003; 10(4): 351 - 362. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. T. Chang, H. Schutze, and R. B. Altman Creating an Online Dictionary of Abbreviations from MEDLINE J. Am. Med. Inform. Assoc., November 1, 2002; 9(6): 612 - 620. [Abstract] [Full Text] [PDF]
|
|
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
