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Early disease outbreak detection systems typically monitor health care data for irregularities by comparing the distribution of recent data against a baseline distribution. Determining the baseline is difficult due to the presence of different trends in health care data, such as trends caused by the day of week and by seasonal variations in temperature and(More)
ICD-9-coded chief complaints and diagnoses are a routinely collected source of data with potential for use in public health surveillance. We constructed two detectors of acute respiratory illness: one based on ICD-9-coded chief complaints and one based on ICD-9-coded diagnoses. We measured the classification performance of these detectors against the human(More)
Traditional biosurveillance algorithms detect disease outbreaks by looking for peaks in a univariate time series of health-care data. Current health-care surveillance data, however, are no longer simply univariate data streams. Instead, a wealth of spatial, temporal, demographic and symptomatic information is available. We present an early disease outbreak(More)
This report describes the design and implementation of the Real-time Outbreak and Disease Surveillance (RODS) system, a computer-based public health surveillance system for early detection of disease outbreaks. Hospitals send RODS data from clinical encounters over virtual private networks and leased lines using the Health Level 7 (HL7) message protocol.(More)
This paper presents an algorithm for performing early detection of disease outbreaks by searching a database of emergency department cases for anomalous patterns. Traditional techniques for anomaly detection are unsatisfactory for this problem because they identify individual data points that are rare due to particular combinations of features. When applied(More)
To assess the value of ICD-9 coded chief complaints for early detection of epidemics, we measured sensitivity, positive predictive value, and timeliness of Influenza detection using a respiratory set (RS) of ICD-9 codes and an Influenza set (IS). We also measured inherent timeliness of these data using the cross-correlation function. We found that, for a(More)
OBJECTIVE Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance. INTRODUCTION Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated medical reports and triage chief(More)
Automatic detection of cases of febrile illness may have potential for early detection of outbreaks of infectious disease either by identification of anomalous numbers of febrile illness or in concert with other information in diagnosing specific syndromes, such as febrile respiratory syndrome. At most institutions, febrile information is contained only in(More)
Early detection and characterization of outdoor aerosol releases of Bacillus anthracis is an important problem. As health departments and other government agencies address this problem with newer methods of surveillance such as environmental surveillance through the BioWatch program and enhanced medical surveillance, they increasingly have newer types of(More)
OBJECTIVE To determine whether sales of electrolyte products contain a signal of outbreaks of respiratory and diarrheal disease in children and, if so, how much earlier a signal relative to hospital diagnoses. DESIGN Retrospective analysis was conducted of sales of electrolyte products and hospital diagnoses for six urban regions in three states for the(More)