Quantifying the determinants of outbreak detection performance through simulation and machine learning.

Journal: Journal of biomedical informatics

Published Date: Nov 6, 2014

Abstract

OBJECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.

Authors

Nastaran Jafarpour

Department of Computer Engineering, Ecole Polytechnique de Montreal, C.P. 6079, succursale Centre-ville, Montreal, Quebec H3C 3A7, Canada. Electronic address: nastaran.jafarpour@polymtl.ca.
Masoumeh Izadi

Department of Epidemiology and Biostatistics, McGill University, Clinical and Health Informatics Research Group, 1140 Pine Ave. West, Montreal, Quebec H3A 1A3, Canada. Electronic address: mtabae@cs.mcgill.ca.
Doina Precup

School of Computer Science, McGill University, 3480 University St., Montreal, Quebec H3A 0E7, Canada. Electronic address: dprecup@cs.mcgill.ca.
David L Buckeridge

Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada.

Keywords

Algorithms Bayes Theorem Computational Biology Computer Simulation Disease Outbreaks False Positive Reactions Humans Machine Learning Probability ROC Curve Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (25445482)

Quantifying the determinants of outbreak detection performance through simulation and machine learning.

Abstract

Authors

Keywords

External Resources

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