Browsing by Author "Pakhomov, Serguei"

Now showing 1 - 7 of 7
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    Artificial Emotional Intelligence: Dialogue Systems in Medicine
    (2019) Arun, Vishnu; Huffstutler, Thomas; Finzel, Raymond; Ferland, Libby; Pakhomov, Serguei; Gini, Maria
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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.
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    Determining Amazon's Mechanical Turk's Survey Response Consistency: A UROP Project
    (2019) Eppel, Jerika; Pakhomov, Serguei; Finzel, Raymond; Kotlyar, Michael
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    Semantic Relatedness and Similarity Reference Standards for Medical Terms
    (2018-05-03) Pakhomov, Serguei; pakh0002@umn.edu; Pakhomov, Serguei; Natural Language Processing / Information Extraction (NLP/IE) Program (Institute for Health Informatics)
    This is a collection of reference standards created to test and validate computerized approaches to quantifying the degree of semantic relatedness and similarity between medical terms. Each dataset consists of a list of term pairs that have been evaluated by various healthcare professionals (e.g., medical coders, residents, clinicians) to determine the degree of semantic relatedness and similarity. The details pertaining to each dataset are provided in the referenced publications.
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    Surgical Action Predicates with Mapping
    (2012-10-31) Wang, Yan; Pakhomov, Serguei; Melton, Genevieve
    The ‘procedure description’ section in operative note contains a significant amount of description of actions performed during an operation. The action predicates (e.g., fill, incision, irrigate, etc.) encode predicative relations between nominal arguments (e.g., chamber, viscoelastic, Murphy hook, L5 root, antibiotic solution). These predicate arguments convey the important details about actions performed during a procedure. This dataset includes frequent action predicates collected from 362,310 operation narratives obtained from University of Minnesota-affiliated Fairview Health Services with the UMLS and SPECIALIST lexicon mapping.
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    UMLS::Similarity: Measuring the Relatedness and Similarity of Biomedical Concepts
    (Association for Computational Linguistics, 2013-06) McInnes, Bridget; Liu, Ying; Pedersen, Ted; Melton, Genevieve; Pakhomov, Serguei
    UMLS::Similarity is freely available open source software that allows a user to measure the semantic similarity or relatedness of biomedical terms found in the Unified Medical Language System (UMLS). It is written in Perl and can be used via a command line interface, an API, or a Web interface.