Learn More
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)
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)
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)
A surge of development of new public health surveillance systems designed to provide more timely detection of outbreaks suggests that public health has a new requirement: extreme timeliness of detection. The authors review previous work relevant to measuring timeliness and to defining timeliness requirements. Using signal detection theory and decision(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)
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)
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully(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)
Electronic laboratory-based reporting, developed by the UPMC Health System, Pittsburgh, Pennsylvania, was evaluated to determine if it could be integrated into the conventional paper-based reporting system. We reviewed reports of 10 infectious diseases from 8 UPMC hospitals that reported to the Allegheny County Health Department in southwestern Pennsylvania(More)