Displaying results 1 - 2 of 2
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Identifying Contextual Features to Improve the Performance of an Influenza-Like Illness Text Classifier
Content Type: Abstract
To understand the types of false positive cases identified by an Influenza-like illness (ILI) text classifier by measuring the prevalence of ILI-related concepts that are negated, hypothetical, include explicit mention of temporality, experienced by… read more… described in templated text that is difficult to process. Document … of these strings. Two reviewers annotated the same document set with a third reviewer completing a blinded … version of the text classifier applied to surveillance document sources was 75% and 27% with 569(4%) false positive … -
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… Enduring Freedom/Operation Iraqi Freedom veterans. Document … to 3314, with an average of 17 symptom annotations per document. The number of annotations (unique mentions) for …