Displaying results 9 - 16 of 17
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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 -
Natural Language Processing: Can it Help Detect Cases and Characterize Outbreaks?
Content Type: Abstract
Objective To demonstrate how natural language processing (NLP) of clinical records can contribute to case detection and characterization in biosurveillance. 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 -
Reducing the Delay in Detecting an Influenza Epidemic with More Sensitive Case Detection Algorithms
Content Type: Abstract
Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition [1]. SSS seek early detection by focusing on pre-diagnostic symptoms that by themselves may not alarm clinicians. We have previously… 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 -
Free-Text Processing To Enhance Detection Of Acute Respiratory Infections
Content Type: Abstract
Objective We asked to what extent computerized processing of the full free-text clinical documentation could enhance syndrome detection compared to the sole use of structured data elements from a comprehensive… read more