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Displaying results 1 - 8 of 8
  • Sensitivity and Specificity of an Ngram Method for Classifying Emergency Department Visits into the Respiratory Syndrome in the Turkish Language

    Content Type: Abstract

    Previously we developed an “Ngram” classifier for syndromic surveillance of emergency department (ED) chief complaints (CC) in Turkish for bioterrorism. The classifier is developed from a set of ED visits for which both the ICD… read more
    … The classifier is developed from a set of ED visits for which both the ICD diagnosis code and CC are … or “out” of the syndrome. It is possible to dichotomize visits “in” or “out” using N-grams by choosing a cut-off …
  • Optimizing Performance of an Ngram Method for Classifying Emergency Department Visits into the Respiratory Syndrome

    Content Type: Abstract

    A number of different methods are currently used to classify patients into syndromic groups based on the patient’s chief complaint (CC). We previously reported results using an “Ngram” text processing program for building classifiers… read more
    … applies the ICD9 classifier to a training set of ED visits for which both the CC and ICD9 code are known. A … Specificity “HEST” (.84) “COUG” (.93) “FEVE” (.47) y Advances in Disease Surveillance 2007;2:1 … A number of …
  • The NGram CC Classifier: A Novel Method of Automatically Creating CC Classifiers Based on ICD9 Groupings

    Content Type: Abstract

    Syndromic surveillance of emergency department (ED) visit data is often based on computer algorithms which assign patient chief complaints (CC) to syndromes. ICD9 code data may also be used to develop visit classifiers for syndromic surveillance but… read more
    … Syndromic surveillance of emergency department (ED) visit data is often based on computer algorithms which … applies the ICD9 classifier to a training set of ED visits for which both the CC and ICD9 code are known. A … ly C ou nt s ICD ngram Fig. 2 Daily Counts N gram vs ICD y = 0.56x + 23.68 R = 0.9 0.0 20.0 40.0 60.0 80.0 100.0 …
  • Improvement in Performance of Ngram Classifiers with Frequent Updates

    Content Type: Abstract

      Syndromic surveillance of emergency department(ED) visit data is often based on computerized classifiers which assign patient chief complaints (CC) tosyndromes. These classifiers may need to be updatedperiodically to account for changes… read more
    …   Syndromic surveillance of emergency department(ED) visit data is often based on computerized classifiers which … chief complaints (CC) tosyndromes. These classifiers may need to be updatedperiodically to account for changes … automatically based on a training set of patient visits for which both the CC and ICD9 are available. This …
  • The Performance of a NGram Classifier for Patients' Chief Complaint Based on a Computerized Pick List Entry and Free Text in an Italian Emergency Department

    Content Type: Abstract

    Syndromic surveillance of emergency department (ED) visit data is often based on computer algorithms which assign patient chief complaints (CC) and ICD code data to syndromes. The triage nurse note (NN) has also been used for… read more
    … Syndromic surveillance of emergency department (ED) visit data is often based on computer algorithms which … The classifier is developed from a set of ED visits for which both the ICD diagnosis code and CC are … The classifier is developed from a set of ED visits for which both the ICD diagnosis code and CC are …
  • Talking Turkish: Using N-Grams for Syndromic Surveillance in a Turkish Emergency Department without the Need for English Translation

    Content Type: Abstract

    Previously we used an “N-Gram” classifier for syndromic surveillance of emergency department (ED) chief complaints (CC) in English for bioterrorism. The classifier is trained on a set of ED visits for which both the ICD diagnosis code and CC are… read more
    … bioterrorism. The classifier is trained on a set of ED visits for which both the ICD diagnosis code and CC are … bioterrorism. The classifier is trained on a set of ED visits for which both the ICD diagnosis code and CC are … CC classifier versus RESP ICD10 Grouping (Test Set) y = 0.642x + 3.0498 R = 0.88 0 5 10 15 20 25 30 35 0 5 10 15 …
  • An Adaptive Anomaly Detection Algorithm

    Content Type: Abstract

    Ideal anomaly detection algorithms shoulddetect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. The algorithms should also be easy to use. Our objective was to develop an anomaly… read more
    … C2 or C3 alert. The test data were time series of daily visits from two emergency departments (ED) for two … with the usual 2.0 cut-off for two EDs. ORIGINAL GI DAILY VISITS, ~19 MONTHS, ED1 GI 4-DAY OUTBREAKS ROC COMPARISON …
  • A System for Simulation: Introducing Outbreaks into Time Series Data

    Content Type: Abstract

    Objective Several authors have described ways to introduce artificial outbreaks into time series for the purpose of developing, testing, and evaluating the effectiveness and timeliness of anomaly detection algorithms, and more… read more
    … and resistant data analysis, we calculated the size Y of the outbreak from the time series using a rule derived … the efficiency of using time series data several fold com- pared to when just a single artificial outbreak is … Figure 1: Comparative Time Series of Emergency Department Visits with Artificial Outbreaks RESULTS Data sequences …