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Methods: The most frequently occurring abbreviations and acronyms from 352,267 dictated clinical notes were used to create a clinical sense inventory. Senses of each abbreviation and acronym were manually annotated from 500 random instances and lexically matched with long forms within the Unified Medical Language System (UMLS V.2011AB), Another Database of Abbreviations in Medline (ADAM), and Stedman's Dictionary, Medical Abbreviations, Acronyms & Symbols, 4th edition (Stedman's). Redundant long forms were merged after they were lexically normalized using Lexical Variant Generation (LVG).

Results: The clinical sense inventory was found to have skewed sense distributions, practice-specific senses, and incorrect uses. Of 440 abbreviations and acronyms analyzed in this study, 949 long forms were identified in clinical notes. This set was mapped to 17,359, 5233, and 4879 long forms in UMLS, ADAM, and Stedman's, respectively. After merging long forms, only 2.3% matched across all medical resources. The UMLS, ADAM, and Stedman's covered 5.7%, 8.4%, and 11% of the merged clinical long forms, respectively. The sense inventory of clinical abbreviations and acronyms and anonymized datasets generated from this study are available for public use at ('Sense Inventories', website).

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Conclusions: Clinical sense inventories of abbreviations and acronyms created using clinical notes and medical dictionary resources demonstrate challenges with term coverage and resource integration. Further work is needed to help with standardizing abbreviations and acronyms in clinical care and biomedicine to facilitate automated processes such as text-mining and information extraction.

Stedman's Pocket Medical Abbreviations contains over 30,000 entries for medical language specialists who need a quick, reliable guide to the spelling and meanings of abbreviations, acronyms, and symbols. This pocket-sized and affordable version of Stedman's Abbreviations, Acronyms & Symbols, Third Edition focuses on the most commonly used abbreviations in all medical specialties. All entries have been thoroughly reviewed for accuracy and currency. The Joint Commission on Accreditation of Healthcare Organization's and the Institute for Safe Medication Practices's dangerous and "do not use" abbreviations are clearly identified in red.

Stedman's Medical Abbreviations, Acronyms and Symbols, Fourth Edition offers quick, easy access to over 75,000 essential abbreviations, acronyms, and symbols for medical language specialists and medical, health, and nursing professionals. This thoroughly updated edition includes more than 7,500 new abbreviations, expansions, acronyms, and symbols and 14 appendices. "Do Not Use" abbreviations indicated by the Institute for Safe Medication Practices are highlighted, as are slang abbreviations.

When transcribing results of arterial blood gas tests, which abbreviation is correct: PAO2 or PaO2? Each of these has different meanings, according to the website I use for acronyms/abbreviations. Actually, I have always typed PAO2, don't know if this is correct, and have never heard otherwise.

Oops! I meant, in the discharge summary under the hospital course heading must we expand dictated abbreviations as we would under the impression or diagnosis? Or is it acceptable to leave them verbatim.

The ShARe/CLEF eHealth challenge lab aims to stimulate development ofnatural language processing and information retrieval technologies to aidpatients in understanding their clinical reports. In clinical text, acronymsand abbreviations, also referenced as short forms, can be difficult forpatients to understand. For one of three shared tasks in 2013 (Task 2), wegenerated a reference standard of clinical short forms normalized to theUnified Medical Language System. This reference standard can be used toimprove patient understanding by linking to web sources with lay descriptionsof annotated short forms or by substituting short forms with a moresimplified, lay term.

Patient access to easy-to-understand, simple text in clinical reports is alsostipulated in several countries by law. For instance, regulations in theUnited States [9] and European Union [10] state that patients should haveaccess to their clinical information upon request [11]. United Kingdomguidelines describe best practices for patient access [12]. Laws and statutesin Sweden [13] and Finland [14] state that clinical notes must be explicitand comprehensive, including only well known, accepted concepts andabbreviations.

Automated tools for text simplification can help clinicians comply withregulations and improve information readability for patients. For instance,statistical approaches can identify, reduce, and disambiguate unfamiliarconcepts. Specifically, unsupervised methods and statistical associations canautomatically learn unfamiliar terms, identify potential semanticequivalents, and present lay terms or definitions [15-17]. Textsimplification architectures can analyze, transform, and regenerate sentencesfor patients e.g., simplifying Wall Street Journal sentences for Aphasiapatients [18]. In the biomedical domain, one text simplification tool reducesthe semantic complexity of sentences conveying health content in biomedicalarticles by substituting unfamiliar medical concepts with synonyms or relatedterms, and the syntactic complexity by dividing longer sentences into shorterconstructions [19]. In the clinical domain, a prototype translator reducesthe semantic complexity of clinical texts by replacing abbreviations andother terms with consumer-friendly terms from the Consumer Health Vocabularyand explanatory phrases [20].

Making annotated corpora available to the natural language processingcommunity through shared tasks can further stimulate development oftechnologies in this research area [21]. Like the Message UnderstandingConference (MUC) [22], Text REtrieval Conference (TREC) [23, 24], GenomeInformation Acquisition (GENIA) [25, 26], and Informatics for IntegratingBiology and the Bedside (i2b2) challenges [27-31], the 2013 Shared AnnotatedResources/Conference and Labs of the Evaluation Forum (ShARe/CLEF) eHealthChallenge evaluated participant natural language processing systems against amanually-generated reference standard [32]. The 2013 ShARe/CLEF eHealthChallenge took initial steps toward facilitating patient understanding ofclinical reports by identifying and normalizing mentions of diseases anddisorders to a standardized vocabulary (Task 1) [33], by normalizing acronymsand abbreviations (Task 2) [34], and by retrieving documents from health andmedicine websites for addressing patient-centric questions about diseases anddisorders documented in clinical notes (Task 3) [35]. This paper describesstudies related to Task 2.

We review acronym and abbreviation recognition in the context of textnormalization. We are motivated by the need for creating an annotated corpusof acronyms and abbreviations to encourage the development of naturallanguage processing tools that improve patient understanding and readabilityof clinical texts.

Conceptually disambiguating the meaning of a word or phrase from clinicaltext often involves mapping to a standardized vocabulary [36]. For example,natural language processing tools that normalize words and phrases to UnifiedMedical Language System (UMLS) concept unique identifiers (CUIs) includeIndexFinder [37], KnowledgeMap [38], MetaMap [39], Medical LanguageExtraction and Encoding System (MedLEE) [40] and clinical Text Analysis andKnowledge Extraction System (cTAKES) [41]. Acronyms and abbreviations areshortened words used to represent one or more concepts [42]. Acronyms areformed from the first letters of words in a meaningful phrase ('BP'= Blood Pressure) and can be pronounced as words ('CABG' = Coronary Artery BypassGraft, pronounced cabbage) or letter-by-letter ('TIA' = Transient Ischemic Attack,pronounced T-I-A). Abbreviations are shortened derivations of a word or phrase ('myocardinfarc' = myocardial infarction) and are generally pronounced as their expanded forms ('myocardinfarc', pronounced myocardial infarction). We will refer to acronyms and abbreviations throughout the manuscript asshort forms for brevity and to convey a mixture of both acronyms andabbreviations, including their lexical variants from the clinical text.

We evaluated participant's system performance for normalizingacronyms/abbreviations to Unified Medical Language System CUIs on the testset (Table 1). Compared to the majority sense baseline results, only thehighest performing system by UTHealthCCB.1 showed improvement. Our majoritysense baseline approach results (~70 % accuracy) are comparative topreviously reported clinical majority sense baseline results (71 % accuracy)[53]. On the training set, THCIB reports 20 % of the short forms from asentence input could not be mapped to CUIs using clinical Text Analysis andKnowledge Extraction System. We believe this demonstrates that out-of-the-boxtext normalization systems will perform moderately for normalizing shortforms. Many participants incorporated clinical Text Analysis and KnowledgeExtraction System pre-processing, conditional random field, and customdictionaries from training data and online resources to develop theirsystems.

49. Yu H, Kim W, Hatzivassiloglou V, Wilbur WJ. Using MEDLINE as a knowledgesource for disambiguating abbreviations and acronyms in full-text biomedicaljournal articles. J Biomed Inform. 2007;40(2):150.-9 doi:10.1016/j.jbi.2006.06.001.

51. Wu Y, Denny JC, Rosenbloom ST, Miller RA, Giuse DA, Xu H. A comparativestudy of current clinical natural language processing systems on handlingabbreviations in discharge summaries. AMIA Annu Symp Proc. 2012; 997-1003

64. Patrick JD, Safari L, Ou Y. ShARe/CLEF eHealth 2013 Normalization ofacronyms/abbreviations challenge. In P Forner, R Navigli, D Tufis (Eds.) CLEF2013 Evaluation Labs and Workshops: Working Notes. Valencia, Spain, 23-26September 2013. -initiative.eu/documents/71612/3f35de3a-7622-4c8e-a515-905aa4d8e6ff (Accessed 15 Apr 2014) e24fc04721