Syndrome Definition

Our objective was to adapt the city'­s syndromic surveillance system to help guide a violence intervention initiative in response to an upsurge in serious assaults and homicides in Boston.

During influenza season, the Boston Public Health Commission uses syndromic surveillance to monitor Emergency Department visits for chief complaints indicative of influenza-like illness (ILI). We created three syndrome definitions for ILI to capture variable presentations of disease, and compared the trends with Boston pneumonia and influenza mortality data, and onset dates for reported cases of influenza.

Objective

To evaluate the impact of different syndrome definitions for ILI by comparing weekly trends with other data sources during the 2005-2006 influenza season in Boston.

We report here on the use of the North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS, www.ncbeips.org) to reverse engineer a syndrome definition of influenza for the purpose of influenza surveillance.

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 (adapted from business research technology at AT&T Labs). The method applies the ICD9 classifier to a training set of ED visits for which both the CC and ICD9 code are known. A computerized method is used to automatically generate a collection of CC substrings (or Ngrams), with associated probabilities, from the training data. We then generate a CC classifier from the collection of Ngrams and use it to find a classification probability for each patient. Previously, we presented data showing good correlation between daily volumes as measured by the Ngram and ICD9 classifiers.

Objective

Our objective was to determine the optimized values for the sensitivity and specificity of the Ngram CC classifier for individual visits using a ROC curve analysis. Points on the ROC curve correspond to different classification probability cutoffs.

Free-text emergency department triage chief complaints (CCs) are a popular data source used by many syndromic surveillance systems because of their timeliness, availability, and relevance. The lack of standardization of CC vocabulary poses a major technical challenge to any automatic CC classification approach. This challenge can be partially addressed by several methods, for example, medical thesaurus, spelling check, manually-created synonym list, and supervised machine learning techniques that directly operate on free text. Current approaches, however, ignore the fact that medical terms appearing in CCs are often semantically related. Our research exploits such semantic relations through a medical ontology in the context of automatic CC classification for syndromic surveillance.

Objective

This paper presents a novel approach of using a medical ontology to classify free-text CCs into syndrome categories.

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 the presence of a syndromic finding (e.g., cough or diarrhea) and a fever.

 

Objective

Automated syndromic surveillance systems often use chief complaints as input. Our objective was to determine whether chief complaints accurately represent whether a patient has any of the following febrile syndromes: Febrile respiratory, febrile gastrointestinal, febrile rash, febrile neurological, or febrile hemorrhagic.