Eliot Siegel

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While clinical text NLP systems have become very effective in recognizing named entities in clinical text and mapping them to standardized terminologies in the normalization process, there remains a gap in the ability of extractors to combine entities together into a complete semantic representation of medical concepts that contain multiple attributes each(More)
—Physicians are often required to make critical medical decisions that may be based on previous events in the patient's health history. However, these events may be very difficult to locate in the patient record due to the large volume of unstructured textual data in the patient's chart. Even when the chart is housed in an electronic health record (EHR)(More)
The recent years have seen a surge in the implementation of electronic health care records. These patient records contain valuable medical information including patient information, diagnosis, treatment methods, and eventual patient outcomes. It is important to analyze patterns within these records in order to more efficiently treat individuals. In this(More)
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