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Displaying results 233 - 240 of 312
  • Delineating Spatial Clusters with Artificial Neural Networks

    Content Type: Abstract

    Multiple or irregularly shaped spatial clusters are often found in disease or syndromic surveillance maps. We develop a novel method to delineate the contours of spatial clusters, especially when there is not a clearly dominating primary cluster,… read more
    … We start de- fining a MLP artificial neural network with training set size m. The geographic coordinates and the scan … backpropagation [2,3]. Follow- ing the training phase, the scan function evaluation is extended for … [2] M. H. Fun and M.T. Hagan, 1996. Levenberg-Marquardt training for modular networks. In Proceedings of the IEEE …
  • Mixture Likelihood Ratio Scan Statistic for Disease Outbreak Detection

    Content Type: Abstract

    This article describes the methodology and results of Team #134ís submission to the 2007 ISDS Technical Contest.
    … sales (OTC), and nurse hotline calls (TH)). The training data included 30 outbreak signatures for each … nent. Guided by the distinct outbreak signatures in the training data, we assumed parametric forms for the outbreak … 1 shows the result of the parametric fit for the first training outbreak. Our contest score was 5.58 for ED, 24.00 …
  • Integrating Early Event Detection into Local Disease Surveillance and Response

    Content Type: Abstract

    This poster describes the practical integration of Early Event Detection (EED) into the daily operation of a medium sized public health department to improve surveillance for, and response to, outbreaks of communicable disease.
    … for simple and rapid checking by someone with limited training. Automated daily emergency department reports are …
  • Use of Epidemiological Knowledge to Create Syndromic Surveillance Reports

    Content Type: Abstract

    Syndromic surveillance is an investigational approach used to monitor trends of illness in communities. It relies on pre-diagnostic health data rather than laboratory-confirmed clinical diagnoses. Its primary purpose is to detect… read more
    … for the Early Notification of Community Based Epidemics (ESSENCE) BACKGROUND Syndromic surveillance is an … necessary. In addition, the epidemiologist analyzing ESSENCE also monitors the county’s 911 Call Center and …
  • Fast Graph Structure Learning from Unlabeled Data for Outbreak Detection

    Content Type: Abstract

    Disease surveillance data often has an underlying network structure (e.g. for outbreaks which spread by person-to-person contact). If the underlying graph structure is known, detection methods such as GraphScan (1) can be used to identify an… read more
    … connected subgraph for each graph structure and each training example using GraphS- can. We normalize each score … by the maximum unconstrained subset score for that training example (computed efficiently using LTSS). We then compute the mean normalized score averaged over all training examples. If a given graph is close to the true …
  • Results from the BioSense Jurisdiction-Specific Wbinars

    Content Type: Abstract

    BioSense is a Centers for Disease Control and Prevention (CDC) national near real-time public health surveillance system. CDC’s BioIntelligence Center (BIC) analysts monitor, analyze, and interpret BioSense data daily and provide support to BioSense… read more
    … hospital utilization and mortality data. Identified training needs included the following: 1) how to use the … up during an event, and 2) self- paced, interactive training materials and tools that will enable users to … this level of dialogue, as well as develop additional training tools to provide ongoing support for our users. …
  • 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 …