Seunghee Kim

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OBJECTIVE To determine whether SVM-based classifiers, which are trained on a combination of inclusion and common exclusion articles, are useful to experts reviewing journal articles for inclusion during new systematic reviews. METHODS Test collections were built using the annotated reference files from 19 procedure and 4 drug systematic reviews. The(More)
OBJECTIVES Machine learning systems can considerably reduce the time and effort needed by experts to perform new systematic reviews (SRs). This study investigates categorization models, which are trained on a combination of included and commonly excluded articles, which can improve performance by identifying high quality articles for new procedures or drug(More)
Objective: CDA (Clinical Document Architecture) is a markup standard for clinical document exchange. In order to increase the semantic interoperability of documents exchange, the clinical statements in the narrative blocks should be encoded with code values. Natural language processing (NLP) is required in order to transform the narrative blocks into the(More)
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