A Bayesian biosurveillance method that models unknown outbreak diseases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4506 LNCS, Issue , 2007, Pages 209-215

  Shen, Yanna ^a   Cooper, Gregory F ^a  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

Anomaly detection; Bayesian methods; Biosurveillance

Indexed keywords

ALGORITHMS; BAYESIAN NETWORKS; DATA ACQUISITION;

ANOMALY DETECTION; BIOSURVEILLANCE; INFLUENZA;

DISEASES;

EID: 37249063105     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72608-1_21     Document Type: Conference Paper

References (6)

1. Wong, W.-K., Data mining for early disease outbreak detection [Doctoral Dissertation]. 2004, Carnegie Mellon University: Pittsburgh.
2. Fawcett, T. and F. Provost, Adaptive fraud detection. Data Mining and Knowledge Discovery, 1997.1(3): p. 291-316.
3. Denning, D., An intrusion-detection model. IEEE Transactions on Software Engineering, 1987.
4. Cooper, G.F., A Bayesian method for learning belief networks that contain hidden variables. Journal of Intelligent Information Systems, 1995. 4: p. 71-88.
5. Shen, Y. and G.F. Cooper, Bayesian disease outbreak detection that includes a model of unknown disease. Department of Medical Informatics, University of Pittsburgh: Technical Report No. DBMI-07-351, 2007.
6. Hutwagner, L., W. Thompson, and G.M. Seeman, The bioterrorism preparedness and response early aberration reporting system (EARS). Journal of Urban Health, 2003. 80 (2, Supplement 1): p. i89-i96.