The Performance of Sub-Syndrome Chief Complaint Classifiers for the GI and RESP Syndromes

The Centers for Disease Control and Prevention BioSense has developed chief complaint (CC) and ICD9 sub syndrome classifiers for the major syndromes for early event detection and situational awareness. The prevalence of these sub-syndromes in the emergency department population and the performance of these CC classifiers have been little studied. Chart reviews have been used in the past to study this type of question but because of the large number of cases to review, the labor involved would be prohibitive.

July 30, 2018

Visits with Nontyphoidal Salmonella Infections Reported to the BioSense System, 2006-2007

Biosurveillance systems typically receive free- text chief complaint and coded diagnosis data, however this data has limited specificity for notifiable disease surveillance. The Biosense System receives chief complaint and/or diagnosis data from over 360 hospitals and laboratory results from 24 hospitals in 7 states using the Public Health Information Network Messaging System (PHINMS) and HL7 standards. BioSense also receives final diagnosis from Veterans’ Affairs and Department of Defense outpatient clinics, but these clinics do not currently report laboratory findings.

March 26, 2019

The Use of BioSense Data for Surveillance of Gastrointestinal Illness

The BioSense system currently receives real-time data from more than 370 hospitals, as well as national daily batched data from over 1100 Department of Defense and Veterans Affairs medical facilities. BioSense maps chief complaint and diagnosis data to 11 syndromes and 78 sub-syndromes (indicators). One of the 11 syndromes is gastrointestinal (GI) illness and 6 of the subsyndromes (abdominal pain; anorexia, diarrhea, food poisoning, intestinal infections, ill-defined; and nausea and vomiting) represent gastrointestinal concepts.



July 30, 2018

Performance Characteristics of Control Chart Detection Methods

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 that accounts for total visits. These modifications produce lower residuals (observed minus expected counts), but their effect on sensitivity has not been studied.



To evaluate several variations of a commonlyused control chart method for detecting injected signals in 2 BioSense System datasets.

July 30, 2018

Rapid Identification of Pneumonias in BioSense Data Using Radiology Text Reports

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 can manifest as pneumonia. Radiology text reports are often received within 1-2 days and may provide a faster way to identify pneumonia than coded diagnoses. Objective To study the performance of a simple keyword search of radiology reports for identifying pneumonia.

July 30, 2018

Identifying Fractures in BioSense Radiology Reports

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.

July 30, 2018

Preliminary Findings from the BioSense Evaluation Project

In October 2006, the Centers for Disease Control and Prevention funded four institutions, including Emory University, to conduct evaluations of the BioSense surveillance system.

July 30, 2018

The Predictive Accuracy of Non-Regression Data Analysis Methods

Analysis of time series data requires accurate calculation of a predicted value. Non-regression methods such as the Early Aberration Reporting System CuSum are computationally simple, but most do not adjust for day of week or holiday. Alternately, regression methods require larger counts, more computer resources, and possibly longer baseline periods of data. As increasing volumes of data are reported and analyzed, the predictive accuracy of simpler methods should be assessed and optimized.



July 30, 2018

ICD-9 CM Based Sub-Syndrome Distributions in BioSense Hospital Data

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.

July 30, 2018

Evaluation of Spatial Estimation Methods for Cluster Detection

CDC’s BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Spatial approaches depend strongly on having reliable estimated values for counts among the geographic sub-regions. If estimates are poor, algorithms will find irrelevant clusters, and clusters of importance may be missed.

July 30, 2018


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