Displaying results 25 - 32 of 32
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Performance Characteristics of Control Chart Detection Methods
Content Type: Abstract
To recognize outbreaks so that early interventions can be applied, BioSense uses a modification of the EARS C2 method, stratifying days used to calculate the expected value by weekend vs weekday, and including a rate-based method… read more -
Performance of Sub-Syndrome Chief Complaint Classifiers for the GI Syndrome
Content Type: Abstract
The Centers for Disease Control and Prevention BioSense project has developed chief complaint (CC) and ICD9 sub-syndrome classifiers for the major syndromes for early event detection and situational awareness. This has the potential to… read more -
Rapid Identification of Pneumonias in BioSense Data Using Radiology Text Reports
Content Type: Abstract
BioSense currently receives demographic and chief complaint data from more than 360 hospitals and text radiology reports from 36 hospitals. Detection of pneumonia is an important as several Category A bioterrorism diseases as well as avian influenza… read more -
Representativeness of Emergency Department Data Reported to the BioSense System
Content Type: Abstract
In 2007, the CDC BioSense System received data from 450 non-federal hospitals. Hospitals provide data to Biosense based on their capability and willingness to supply electronic data. As of July 2008, Biosense is receiving data from 550 hospitals.… read more -
Results from the BioSense Jurisdiction-Specific Wbinars
Content Type: Abstract
BioSense is a Centers for Disease Control and Prevention (CDC) national near real-time public health surveillance system. CDC’s BioIntelligence Center (BIC) analysts monitor, analyze, and interpret BioSense data daily and provide support to BioSense… read more -
ICD-9 CM Based Sub-Syndrome Distributions in BioSense Hospital Data
Content Type: Abstract
Objective To examine sub-syndrome distributions among BioSense emergency department (ED) chief complaint and final diagnosis based data and to observe patterns by hospital system, age, and gender. -
Identifying Clusters of Falls During the 2007-08 Winter Season in the BioSense System
Content Type: Abstract
The purposes of this study are to identify and characterize increases in emergency department (ED) visits for falls during the 2007-08 winter season. -
Identifying Fractures in BioSense Radiology Reports
Content Type: Abstract
The purposes of this study are to validate a keyword-based text parsing algorithm for identifying fractures and compare radiology results with chief complaint and ICD-9 final diagnoses.