Browsing by Author "Moon, Sungrim"

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    Automatic word sense disambiguation of acronyms and abbreviations in clinical texts
    (2012-12) Moon, Sungrim
    The use of acronyms and abbreviations is increasing profoundly in the clinical domain in large part due to the greater adoption of electronic health record (EHR) systems and increased electronic documentation within healthcare. A single acronym or abbreviation may have multiple different meanings or senses. Comprehending the proper meaning of an acronym or abbreviation using automated machine techniques, known as word sense disambiguation (WSD), in clinical notes is an essential step for medical natural language processing (NLP) systems. While acronym and abbreviation WSD from the biomedical literature is an active area of investigation, little research has been done on this topic with clinical documents. The purpose of this dissertation is to develop automatic WSD tools for clinical acronyms and abbreviations. A key step toward this end is to build a comprehensive clinical sense inventory based upon the integration of available biomedical resources and upon senses from a large corpus of clinical notes. Another complementary task is the performance maximization of machine learning (ML) algorithms. This includes the development of optimal feature sets for WSD and the exploration of minimum "adequate" sample size for training classifiers. These automatic WSD technologies extend to the complementary problem of symbol disambiguation in clinical texts. Lastly, the anticipated future work will be in developing quality improvement of automatic WSD tools including sense amelioration utilizing biomedical knowledge.
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    Clinical Abbreviation Sense Inventory
    (2012-10-31) Moon, Sungrim; Pakhomov, Serguei; Melton, Genevieve
    A sense inventory is a collection of abbreviations and acronyms (short forms) with their possible senses (long forms), along with other corresponding information about these terms. For our comprehensive sense inventory for clinical abbreviations and acronyms, a total of 440 most frequently used abbreviations and acronyms were selected from 352,267 dictated clinical notes. 949 senses of each abbreviation and acronym were manually annotated from 500 random instances within clinical notes and lexically aligned with 17,359 long forms of the Unified Medical Language System (UMLS), 5,233 long forms of Another Database of Abbreviations in Medline (ADAM), and 4,879 long forms in Stedman’s Medical Abbreviations, Acronyms & Symbols (4th edition).
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    Clinical Symbol Sense Inventory
    (2012-10-31) Moon, Sungrim; Pakhomov, Serguei; Melton, Genevieve
    Although clinical texts contain many symbols, relatively little attention has been given to symbol resolution by medical natural language processing (NLP) researchers. Interpreting the meaning of symbols may be viewed as a special case of Word Sense Disambiguation (WSD). One thousand instances of four common non-alphanumeric symbols (‘+’, ‘–’, ‘/’, and ‘#’) were randomly extracted from a clinical document repository and annotated by experts. De-identified data are available for researchers.