Name
Title | ||
|---|---|---|
Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels. |
Abstract | ||
|---|---|---|
Objective We discuss the use of structural models for the analysis of biosurveillance related data. Methods and results Using a combination of real and simulated data, we have constructed a data set that represents a plausible time series resulting from surveillance of a large scale bioterrorist anthrax attack in Miami. We discuss the performance of anomaly detection with structural models for these data using receiver operating characteristic (ROC) and activity monitoring operating characteristic (AMOC) analysis. In addition, we show that these techniques provide a method for predicting the level of the outbreak valid for approximately 2 weeks, post-alarm. Conclusions Structural models provide an effective tool for the analysis of biosurveillance data, in particular for time series with noisy, non-stationary background and missing data. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1136/amiajnl-2012-000945 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | Field | DocType |
anomaly detection,kalman filter,time series | Data mining,Anomaly detection,Receiver operating characteristic,Kalman filter,Outbreak,Missing data,Biosurveillance,Medicine | Journal |
Volume | Issue | ISSN |
20 | 3 | 1067-5027 |
Citations | PageRank | References |
2 | 0.42 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karen Elizabeth Cheng | 1 | 2 | 0.42 |
| David J. Crary | 2 | 2 | 0.42 |
| Jaideep Ray | 3 | 198 | 24.42 |
| Cosmin Safta | 4 | 33 | 8.56 |