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Syndromic Surveillance from Free-text Triage Chief Complaints

Motivation. Phase III Evaluation: Outbreak Detection. Phase I Evaluation: Feature Detection. Phase II Evaluation: Case Detection. Syndromic Representation. Respiratory Gastrointestinal Neurological Rash. Botulinic Hemorrhagic Constitutional Other. Center for Biomedical

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Syndromic Surveillance from Free-text Triage Chief Complaints

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  1. Motivation Phase III Evaluation:Outbreak Detection Phase I Evaluation:Feature Detection Phase II Evaluation:Case Detection Syndromic Representation • Respiratory • Gastrointestinal • Neurological • Rash • Botulinic • Hemorrhagic • Constitutional • Other Center for Biomedical Informatics Syndromic Surveillance from Free-text Triage Chief Complaints Wendy W. Chapman PhD, Michael M. Wagner MD PhD, Oleg Ivanov MD MPH, Robert Olszewski PhD, John N. Dowling MD University of Pittsburgh RODS Laboratory AdmitICD-9 Codes484.5 Free-textChief Complaints“cough and fever” • Syndromic Surveillance • Diseases initially present as syndromes • Increase in expected syndromic presentation for a given population could indicate outbreak • Free-text Chief Complaints • One of earliest pieces of clinical data available • Generated by most hospitals/urgent care facilities • Short phrases easier to classify into syndromes • Goal: Identify patients who present to the emergency room with a syndrome of interest • Study 1: identify patients with acute, infectious gastrointestinal (GI) disorder • Two Sources of Input Data • ICD-9 admit codes- expert-compiled list of ICD-9 acute GI classifier codes • Free-text triage chief complaints- classified into syndromes by NB classifier • Methods • Task: Compare predictive performance of the two sources of input data at identifying patients with acute, infectious GI disorder • Test Set: 1,425 randomly selected patients admitted to the emergency department at UPMC Presbyterian in 2000 • Gold Standard: Majority vote of three physicians reading the patients’ emergency department dictations • Outcome Measures: Sensitivity, Specificity, PPV, NPV • Results Syndromic Classifiers • Goal: Classify free-text chief complaint strings into syndromes • “Bad cough and fever” = Respiratory • Two Text Processing Syndromic Classifiers • Natural language processor (M+) • Naïve Bayesian text classifier (NB) • Methods • Task: classify chief complaints into one of 8 syndromic representations • Gold standard: physician classifications • Outcome measure: area under the ROC curve (AUC) • Results Detection Algorithms Goal: Identify retrospective and prospective outbreaks using free-text triage chief complaints Work in progress Alarms * There were no Botulinic test cases for M+

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