Multivariate Bayesian modeling of known and unknown causes of events-An application to biosurveillance

Authors:
Yanna Shen;Gregory F. Cooper
Affiliations:
The Lister Hill National Center for Biomedical Communications, Bethesda, MD, USA;Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

Citing 8
Cited 0

An Intrusion-Detection Model

IEEE Transactions on Software Engineering - Special issue on computer security and privacy
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Machine Learning - Special issue on learning with probabilistic representations
Adaptive Fraud Detection

Data Mining and Knowledge Discovery
Fever detection from free-text clinical records for biosurveillance

Journal of Biomedical Informatics
Data mining for early disease outbreak detection

Data mining for early disease outbreak detection
Bayesian biosurveillance of disease outbreaks

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks

The Journal of Machine Learning Research
Bayesian modeling of anomalies due to known and unknown causes

Bayesian modeling of anomalies due to known and unknown causes

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper investigates Bayesian modeling of known and unknown causes of events in the context of disease-outbreak detection. We introduce a multivariate Bayesian approach that models multiple evidential features of every person in the population. This approach models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A contribution of this paper is that it introduces a multivariate Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has general applicability in domains where the space of known causes is incomplete.