Displaying results 1 - 3 of 3
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A Bayesian Algorithm for Detecting CDC Category A Outbreak Diseases from Emergency Department Chief Complaints
Content Type: Abstract
This paper describes a Bayesian algorithm for diagnosing the CDC Category A diseases, namely, anthrax, smallpox, tularemia, botulism and hemorrhagic fever, using emergency department chief complaints. The algorithm was evaluated on real data and on… read more -
An Outbreak Detection Algorithm that Efficiently Performs Complete Bayesian Model Averaging Over All Possible Spatial Distributions of Disease
Content Type: Abstract
Many disease-outbreak detection algorithms, such as control chart methods, use frequentist statistical techniques. We describe a Bayesian algorithm that uses data D consisting of current day counts of some event (e.g., emergency department (ED)… read more -
SyCo: A Probabilistic Machine Learning Method for Classifying Chief Complaints into Symptom and Syndrome Categories
Content Type: Abstract
Scientists have utilized many chief complaint (CC) classification techniques in biosurveillance including keyword search, weighted keyword search, and naïve Bayes. These techniques may utilize CC-to-syndrome or CC-… read more