Displaying results 1 - 6 of 6
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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… Scientists have utilized many chief complaint (CC) classification techniques in biosurveillance … search, and naïve Bayes. These techniques may utilize CC-to-syndrome or CC-to-symptom-to-syndrome classification approaches. In the … BACKGROUND Scientists have utilized many chief complaint (CC) classification techniques in biosurveillance including … -
An automated influenza-like-illness reporting system using freetext emergency department reports
Content Type: Abstract
Current methods for influenza surveillance include laboratory confirmed case reporting, sentinel physician reporting of Influenza-Like-Illness (ILI) and chief-complaint monitoring from emergency departments (EDs). The current… read more… to incomplete, delayed reporting. Chief complaint (CC) based surveillance is limited in that a patient’s … the cost, delays, incompleteness and low specificity (for CC) in current methods of influenza surveillance is … to incomplete, delayed reporting. Chief complaint (CC) based surveillance is limited in that a patient’s chief … -
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… based on free-text chief complaints. Chief complaints (CC) demonstrate low to moderate sensitivity in identifying … based on free-text chief complaints. Chief complaints (CC) demonstrate low to moderate sensitivity in identify- ing … (lower three diamonds). ED Classifi- cation dominated CC Classification by a physician (), CoCo ( ), and MPLUS ( … -
Building an automated Bayesian case detection system
Content Type: Abstract
Current practices of automated case detection fall into the extremes of diagnostic accuracy and timeliness. In regards to diagnostic accuracy, electronic laboratory reporting (ELR) is at one extreme and syndromic surveillance is at… read more… immediate, and ELR is delayed 7 days from initial patient visit. A plausible solution, a middle way, to the extremes … mediate, and ELR is delayed 7 days from initial patient visit.1 A plausible solution, a middle way, to the extremes … Figure 1 shows chart of percent daily expected ED flu visits from July to December of 2009. Average daily ED … -
Modeling 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 more… the model assumes that a patient is only seen by a health care provider once (no return visits) and each clinician diagnoses a given case of IA … anthrax. Ann NY Acad Sci. 1980;353:83-93. 4. Penn CC, Klotz SA. Anthrax pneumonia Sem Resp Med 1997;12:28-30. … -
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… used a previously developed anthrax out- break simulator (BARD), along with chief complaint probabilities taken from …