Displaying results 1 - 7 of 7
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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 -
Clinical decision support at the time of an e-prescription can sustainably decrease unwarranted use of antibiotics for acute respiratory infections
Content Type: Abstract
Microorganisms resistant to antibiotics (ABX) increase the mortality, morbidity and costs of infections. In the absence of a drug development pipeline that can keep pace with the emerging resistancemechanisms, these organisms are expected… 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 -
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 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 -
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