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Displaying results 65 - 72 of 121
  • Anomaly Pattern Detection for Biosurveillance

    Content Type: Abstract

    We propose a new method for detecting patterns of disease cases that correspond to emerging outbreaks. Our Anomaly Pattern Detector (APD) first uses a "local anomaly detector" to identify individually anomalous records and then searches over subsets… read more
    … particular rules for the current (test) and historical (training) datasets. How- ever, an outbreak may create a … we compare it to the corres- ponding subset in the training data. For each rule R, we determine the total number of corresponding records in the test and training datasets (C(R)test and C(R)train) and the number of …
  • Evaluation of Preprocessing Techniques for Chief Complaint Classification

    Content Type: Abstract

    The Real-time Outbreak and Disease Surveillance system collects chief complaints as free text and uses a naïve Bayesian classifier called CoCo to classify the complaints into syndromic categories. CoCo 3.0 has been trained on 28,990 manually clas-… read more
    … 10,161 chief complaints not previously involved in CoCo’s training to measure the propor- tion of chief complaints … We counted the number of unique words in the train- ing set for CoCo 3.0 prior to and post preprocessing, … plaints and decreased the number of unique words in the training set from 2,775 to 2,308. All the words changed in …
  • Infection Control Practitioner Use of NC DETECT

    Content Type: Abstract

    The UNC Department of Emergency Medicine (UNC DEM) conducted an online survey to better understand the surveillance needs of Infection Control Practitioners (ICPs) in North Carolina and solicit feedback on the utility of the North Carolina Disease… read more
    … of the reports available to ICPs and provide targeted training on the specific surveillance needs of ICPs. In … Program for Infection Control and Epidemiology (SPICE). Training should focus on the tools in NC DETECT that assist …
  • Learning Specific Detectors of Adverse Events in Multivariate Time Series

    Content Type: Abstract

    This paper describes how powerful detectors of adverse events manifested in multivariate series of bio-surveillance data can be learned using only a few labeled instances of such events.
    … using domain expertise, if the amount of available training data is insufficient to support automated learning … for machine learning techniques which would allow for training specific detectors even if the number of iden- … improvement can be obtained by combining into the training data labels on false posi- tives with one, then …
  • Use of Syndromic Surveillance in the Investigation of Salmonella Wandsworth Outbreak

    Content Type: Abstract

    On June 22, 2007 increases in over-the-counter (OTC) electrolyte and child anti-fever medication sales were detected through routine OTC surveillance. Increases in emergency department (ED) data for gastrointestinal (GI) illness among… read more
    … Early notification of the EDs could also aide in ob- taining clinical specimens as new potential cases pre- sent … from EDs need to be developed, either through staff training or remote access by DOHMH. References: [1] Balter …
  • Access to and use of syndromic surveillance information at the local health department level

    Content Type: Abstract

    Syndromic surveillance data have been widely shown to be useful to large health departments. Use at smaller local health departments (LHDs) has rarely been described, and the effectiveness of various methods of delivering syndromic… read more
    … of syndromic surveillance data, as well as provision of training, may increase use of this information at the local …
  • Expanding a Gazetteer-based Approach for Geo-Parsing Text from Media Reports on Global Disease Outbreaks

    Content Type: Abstract

    HealthMap (www.healthmap.org) is a freely accessible, automated real-time system that monitors, organizes, integrates, filters, and maps online news about emerging diseases. The system performs geographic parsing (“geo-parsing”) of disease… read more
    … outbreaks, retrieved by HealthMap in 2007, is used as the training dataset. The words in the articles are tagged with … The experiments also reveal that providing additional training material improves the performance of the model, …
  • Support Vector Machines for Syndromic Surveillance

    Content Type: Abstract

    Early and reliable detection of anomalies is a critical challenge in disease surveillance. Most surveillance systems collect data from multiple data streams but the majority of monitoring is performed at univariate time series… read more
    … to the algorithm [2] that requires only positive training examples, in our case - the normal (no outbreak) … presented to the epidemiologist. INITIAL RESULTS The training was performed on ESSENCE data with disease …