Sharon Lipsky Gorman

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As more and more information is available in the Electronic Health Record in the form of free-text narrative, there is a need for automated tools, which can process and understand such texts. One first step towards the automated processing of clinical texts is to determine the document-level structure of a patient note, i.e., identifying the different(More)
OBJECTIVE To describe HARVEST, a novel point-of-care patient summarization and visualization tool, and to conduct a formative evaluation study to assess its effectiveness and gather feedback for iterative improvements. MATERIALS AND METHODS HARVEST is a problem-based, interactive, temporal visualization of longitudinal patient records. Using scalable,(More)
We describe two tasks—named entity recognition (Task 1) and template slot filling (Task 2)—for clinical texts. The tasks leverage annotations from the ShARe corpus, which consists of clinical notes with annotated mentions disorders, along with their normaliza-tion to a medical terminology and eight additional attributes. The purpose of these tasks was to(More)
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