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 … -
Text-Processing of VA Clinical Notes to Improve Case Detection Models for Influenza-like Illness
Content Type: Abstract
Objective There were two objectives of this analysis. First, apply text-processing methods to free-text clinician notes extracted from the VA electronic medical record for automated detection of Influenza-Like-Illness. Secondly,… read more… determine if use of data from free-text clinical documents can be used to enhance the predictive ability of … determine if use of data from free-text clinical documents can be used to enhance the predictive ability of … provide a means of utilizing electronic clinical documents as an additional data source for syndromic … -
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… on symptoms, problems, or findings from electronic note documents. False positive extractions may be due to concepts … 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 …