www.julesberman.info/abbtwo.htm
The text that discusses the list and describes the different classes of medical abbreviations is found at:
www.julesberman.info/abb1.htm
This document also describes computational approaches to parsing and disambiguating abbreviations found in medical text.
The abstract for the document is shown here:
Berman JJ. Pathology Abbreviated: A Long Review of Short Terms. Archives of Pathology and Laboratory Medicine, 128:347-352, 2004.
Context.—Abbreviations are used frequently in pathology reports and medical records. Efforts to identify and organize free-text concepts must correctly interpret medical abbreviations. During the past decade, the author has collected more than 12000 medical abbreviations, concentrating on terms used or interpreted by pathologists.
Objective.—The purpose of the study is to provide readers with a listing of abbreviations. The listing of abbreviations is reviewed for the purpose of determining the variety of ways that long forms are shortened.
Design.—Abbreviations fell into different classes. These classes seemed amenable to distinct algorithmic approaches to their correct expansions. A discussion of these abbreviation classes was included to assist informaticians who are searching for ways to write software that expands abbreviations found in medical text. Classes were separated by the algorithmic approaches that could be used to map abbreviations to their correct expansions. A Perl implementation was developed to automatically match expansions with Unified Medical Language System concepts.
Measurements.—The abbreviation list contained 12097 terms; 5772 abbreviations had unique expansions. There were 6325 polysemous abbreviation/expansion pairs. The expansions of 8599 abbreviations mapped to Unified Medical Language System concepts. Three hundred twenty-four abbreviations could be confused with unabbreviated words. Two hundred thirteen abbreviations had different expansions depending on whether the American or the British spellings were used. Nine hundred seventy abbreviations ended in the letter “s.”
Results.—There were 6 nonexclusive groups of abbreviations classed by expansion algorithm, as follows: (1) ephemeral; (2) hyponymous; (3) monosemous; (4) polysemous; (5) masqueraders of common words; and (6) fatal (abbreviations whose incorrect expansions could easily result in clinical errors).
Conclusion.—Collecting and classifying abbreviations creates a logical approach to the development of class-specific algorithms designed to expand abbreviations. A large listing of medical abbreviations is placed into the public domain. The most current version is available at http://www.julesberman.info/abbtwo.htm
In June, 2014, my book, entitled Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases was published by Elsevier. The book builds the argument that our best chance of curing the common diseases will come from studying and curing the rare diseases.
I urge you to read more about my book. There's a generous preview of the book at the Google Books site. If you like the book, please request your librarian to purchase a copy of this book for your library or reading room.
- Jules J. Berman, Ph.D., M.D.
