Displaying results 9 - 15 of 15
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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 -
Using Biosurveillance Whole-System Facsimiles To Compare Aberrancy-Detection Methods: Should BioSense Use SatScan?
Content Type: Abstract
OBJECTIVE A “whole-system facsimile” recreates a complex automated biosurveillance system running prospectively on real historical datasets. We systematized this approach to compare the performance of otherwise… read more -
Using NLP on VA Electronic Medical Records to Facilitate Epidemiologic Case Investigations
Content Type: Abstract
A major goal of biosurveillance is the timely detection of an infectious disease outbreak. Once a disease has been identified, another very important goal is to find all known cases of the disease to assist public health… read more -
Using the Electronic Medical Record to Reduce both the Delay and the Workload Required to Detect and Influenza Epidemic
Content Type: Abstract
Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition. Through a manual electronic medical record (EMR) review of 5,127 outpatient encounters at the Veterans… read more -
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 -
Classifying Supporting, Refuting, or Uncertain Evidence for Pneumonia Case Review
Content Type: Abstract
Characterizing mentions found in clinical texts that support, refute, or represent uncertainty for suspected pneumonia is one area where automated Natural Language Processing (NLP) screening algorithms could be improved. Mentions of uncertainty and… read more -
Extending an Uncertainty Taxonomy for Suspected Pneumonia Case Review
Content Type: Abstract
Natural language processing algorithms that accurately screen clinical documents for suspected pneumonia must extract and reason about whether these mentions provide evidence that supports, refutes, or represents uncertainty. Our efforts extend… read more