Displaying results 1 - 8 of 14
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 moreModeling Clinician Detection Time of a Disease Outbreak Due to Inhalational Anthrax
Content Type: Abstract
We developed a probabilistic model of how clinicians are expected to detect a disease outbreak due to an outdoor release of anthrax spores, when the clinicians only have access to traditional clinical information (e.g., no computer-based alerts). We… read moreA Temporal Method for Outbreak Detection Using a Bayesian Network
Content Type: Abstract
Non-temporal Bayesian network outbreak detection methods only look at data from the most recent day. For example, PANDA-CDCA (PC) only looks at data from the last 24 hours to determine how likely an outbreak is occurring. PC… read moreAn 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 moreChief Complaint Preprocessing Evaluated on Statistical and Non-Statistical Classifiers
Content Type: Abstract
To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance.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 moreA Bayesian Scan Statistic for Spatial Cluster Detection
Content Type: Abstract
This paper develops a new Bayesian method for cluster detection, the ìBayesian spatial scan statistic,î and compares this method to the standard (frequen-tist) scan statistic approach on the task of prospective disease surveillance.A Comparison of Chief Complaints and Emergency Department Reports for Identifying Patients with Acute Lower Respiratory Syndrome
Content Type: Abstract
Automated syndromic surveillance systems often classify patients into syndromic categories based on free-text chief complaints. Chief complaints (CC) demonstrate low to moderate sensitivity in identifying syndromic cases. Emergency Department (ED)… read more