Displaying results 9 - 16 of 19
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The NGram CC Classifier: A Novel Method of Automatically Creating CC Classifiers Based on ICD9 Groupings
Content Type: Abstract
Syndromic surveillance of emergency department (ED) visit data is often based on computer algorithms which assign patient chief complaints (CC) to syndromes. ICD9 code data may also be used to develop visit classifiers for syndromic surveillance but… read more -
The Performance of a NGram Classifier for Patients' Chief Complaint Based on a Computerized Pick List Entry and Free Text in an Italian Emergency Department
Content Type: Abstract
Syndromic surveillance of emergency department (ED) visit data is often based on computer algorithms which assign patient chief complaints (CC) and ICD code data to syndromes. The triage nurse note (NN) has also been used for… read more -
The Performance of Sub-Syndrome Chief Complaint Classifiers for the GI and RESP Syndromes
Content Type: Abstract
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… read more -
The Utility of Patient Chief Complaint and ICD 9 Classifiers for the Influenza Sub-Syndrome
Content Type: Abstract
In order to detect influenza outbreaks, the New York State Department of Health emergency department (ED) syndromic surveillance system uses patients’ chief complaint (CC) to assign visits to respiratory and fever syndromes… read more -
Variation in Visits Classified by GI ICD9 Biosurveillance Sub-Filters as a Function of Age
Content Type: Abstract
The CDC recently developed sub-syndromes for classifying disease to enhance syndromic surveillance of natural outbreaks and bioterrorism. They have developed ICD9 classifiers for six GI Illness subsyndromes: Abdominal Pain,… read more -
Optimizing Performance of an Ngram Method for Classifying Emergency Department Visits into the Respiratory Syndrome
Content Type: Abstract
A number of different methods are currently used to classify patients into syndromic groups based on the patient’s chief complaint (CC). We previously reported results using an “Ngram” text processing program for building classifiers… read more -
Performance of an Adaptive Anomaly Detection Algorithm for a Low Incidence Syndrome Before and After a Major Outbreak
Content Type: Abstract
Ideal anomaly detection algorithms should detect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. Further, the algorithm needs to perform well when the need is to detect… 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