Displaying results 1 - 5 of 5
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Extracting Surveillance Data from Templated Sections of an Electronic Medical Note: Challenges and Opportunities
Content Type: Abstract
The main stay of recording patient data is the free text of electronic medical records (EMR). While stating the chief complaint and history of presenting illness in the patients ‘own words’, the rest of the electronic note is written by the provider… read more -
Automated Detection of GI Syndrome using Structured and Non-Structured Data from the VA EMR
Content Type: Abstract
Objective We performed a gold-standard manual chart review for gastro-intestinal syndrome to evaluate automated detection models based on both structured and non-structured data extracted from the VA … read more -
Pilot Evaluation of Syndrome-specific School Absenteeism Data for Public Health Surveillance
Content Type: Abstract
School absenteeism data could be used as an early indicator for disease outbreaks. The increase in absences, however, may be driven by non-sickness related factors. Reason for absence combined with syndrome-specific information… 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 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