Sungrim Moon

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Acronyms and abbreviations within electronic clinical texts are widespread and often associated with multiple senses. Automated acronym sense disambiguation (WSD), a task of assigning the context-appropriate sense to ambiguous clinical acronyms and abbreviations, represents an active problem for medical natural language processing (NLP) systems. In this(More)
The 2016 Clinical TempEval challenge addresses temporal information extraction from clinical notes. The challenge is composed of six sub-tasks, each of which is to identify: (1) event mention spans, (2) time expression spans, (3) event attributes, (4) time attributes, (5) events’ temporal relations to the document creation times (DocTimeRel), and (6)(More)
OBJECTIVE To create a sense inventory of abbreviations and acronyms from clinical texts. 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(More)
Acronyms are increasingly prevalent in biomedical text, and the task of acronym disambiguation is fundamentally important for biomedical natural language processing systems. Several groups have generated sense inventories of acronym long form expansions from the biomedical literature. Long form sense inventories, however, may contain conceptually redundant(More)
OBJECTIVES Although acronyms and abbreviations in clinical text are used widely on a daily basis, relatively little research has focused upon word sense disambiguation (WSD) of acronyms and abbreviations in the healthcare domain. Since clinical notes have distinctive characteristics, it is unclear whether techniques effective for acronym and abbreviation(More)
A unique characteristic of clinical text is the pervasive use of acronyms and abbreviations, which are often ambiguous. The ShARe/CLEF eHealth Evaluation Lab organized three shared tasks on clinical natural language processing (NLP) and information retrieval (IR) in 2013 and one of them was to normalize acronyms/abbreviations to UMLS concept unique(More)
Automated Word Sense Disambiguation in clinical documents is a prerequisite to accurate extraction of medical information. Emerging methods utilizing hyperdimensional computing present new approaches to this problem. In this paper, we evaluate one such approach, the Binary Spatter Code Word Sense Disambiguation algorithm, on 50 ambiguous abbreviation sets(More)
BACKGROUND Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain. METHODS In this study, we developed the first AL-enabled annotation(More)
A computable knowledge base containing relations between diseases and lab tests would be a great resource for many biomedical informatics applications. This paper describes our initial step towards establishing a comprehensive knowledge base of disease and lab tests relations utilizing three public on-line resources. LabTestsOnline, MedlinePlus and(More)