Cargando…
Algorithm Combination for Improved Performance in Biosurveillance: Univariate Monitoring
This chapter proposes an enhancement to currently used algorithms for monitoring daily counts of pre-diagnostic data. Rather than use a single algorithm or apply multiple algorithms simultaneously, our approach is based on ensembles of algorithms. The ensembles lead to better performance in terms of...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2010
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121873/ http://dx.doi.org/10.1007/978-1-4419-6892-0_8 |
Sumario: | This chapter proposes an enhancement to currently used algorithms for monitoring daily counts of pre-diagnostic data. Rather than use a single algorithm or apply multiple algorithms simultaneously, our approach is based on ensembles of algorithms. The ensembles lead to better performance in terms of higher true alert rates for a given false alert rate. Combinations can be employed at the data preprocessing step and/or at the monitoring step. We discuss the advantages of such an approach and illustrate its usefulness using authentic modern biosurveillance data. |
---|