Name
Abstract | ||
|---|---|---|
We describe a method to improve detection of disease outbreaks in pre-diagnostic time series data. The method uses multiple forecasters and learns the linear combination to minimize the expected squared error of the next day's forecast. This combination adaptively changes over time. This adaptive ensemble combination is used to generate a disease alert score for each day, using a separate multiday combination method learned from examples of different disease outbreak patterns. These scores are used to generate an alert for the epidemiologist practitioner. Several variants are also proposed and compared. Results from the International Society for Disease Surveillance (ISDS) technical contest are given, evaluating this method on three syndromic series with representative outbreaks. |
Year | Venue | Keywords |
|---|---|---|
2008 | AAAI | pre-diagnostic time series data,linear combination,syndromic series,disease outbreak detection,adaptive ensemble combination,ensemble forecasting,different disease outbreak pattern,disease outbreak,next day,disease alert score,separate multiday combination method,combination adaptively change,time series data |
Field | DocType | Citations |
Time series,Linear combination,Data mining,Ensemble forecasting,Computer science,Mean squared error,Disease surveillance,Outbreak,Artificial intelligence,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.35 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas H. Lotze | 1 | 1 | 0.35 |
| Galit Shmueli | 2 | 265 | 23.00 |