Displaying results 1 - 3 of 3
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Using clinician mental models to guide annotation of medically unexplained symptoms and syndromes found in VA clinical documents
Content Type: Abstract
Medically unexplained syndromes (MUS) are conditions that are diagnosed on the basis of symptom constellations and are characterized by a lack of well-defined pathogenic pathways. The three most common MUS are chronic fatigue … read more… consistently applied to identify MUS found in VA clinical documents. These efforts will support building a … to 3314, with an average of 17 symptom annotations per document. The number of annotations (unique mentions) for … -
Identification of features for detection and prediction of homelessness from VA clinical documents
Content Type: Abstract
Homelessness in general is a major issue in the US today. The risk factors of homelessness are myriad, including inadequate income, lack of affordable housing, mental health and substance abuse issues, lack of social support, and nonadherence to… read more… Early identification of these factors from clinical documents may help detect or even predict homelessness … and risk factors for homelessness found in VA clinical documents. This domain knowledge can be used to support … _Detection_And_Prediction_Of_Homelessness_From_Va_Clinical_Documents.pdf Submitted by Magou on Tue, 06/18/2019 - 14:53 … -
Standardization to aid interoperability between NLP system
Content Type: Abstract
There are a number of Natural Language Processing (NLP) annotation and Information Extraction (IE) systems and platforms that have been successfully used within the medical domain. Although these groups share components of their systems, there… read more… IE tools, corpus evaluation tools and encoded clinical documents. There are two components to a successful … community, yet be expressive enough to encode a clinical document, a named entity, relationships between entities, …