Displaying results 9 - 13 of 13
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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 -
Evaluating Syndrome Definitions in the Extended Syndromic Surveillance Ontology
Content Type: Abstract
The Extended Syndromic Surveillance Ontology (ESSO) is an open source terminological ontology designed to facilitate the text mining of clinical reports in English [1,2]. At the core of ESSO are 279 clinical concepts (for example, fever, confusion,… read more -
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 -
Monitoring Febrile Syndromes from Chief Complaints: Is the Information There?
Content Type: Abstract
There exists no standard set of syndromes for syndromic surveillance, and available syndromic case definitions demonstrate substantial heterogeneity of findings constituting the definition. Many syndromic case definitions require… read more -
Identifying Respiratory-Related Clinical Conditions from ED Reports with Topaz
Content Type: Abstract
Case detection from chief complaints suffers from low to moderate sensitivity. Emergency Department (ED) reports contain detailed clinical information that could improve case detection ability and enhance outbreak… read more