AMIA Annu Symp Proc - Using hierarchical mixture of experts model for fusion of outbreak detection methods.

Tópicos

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Resumo

A wide variety of disease outbreak detection methods has been developed in automated public health surveillance systems. The choice of outbreak detection method results in large changes in performance under different circumstances. In this paper, we investigate how outbreak detection methods can be combined in order to improve the overall detection performance. We used Hierarchical Mixture of Experts, which is a probabilistic model for combining classification methods, for fusion of detection methods. Simulated surveillance data for waterborne disease outbreaks are used in this research to train and evaluate a Hierarchical Mixture of Experts model. Performance evaluation of our approach with respect to sensitivity-specificity trade-off and detection timeliness is provided in comparison with several other detection methods.

Resumo Limpo

wide varieti diseas outbreak detect method develop autom public health surveil system choic outbreak detect method result larg chang perform differ circumst paper investig outbreak detect method can combin order improv overal detect perform use hierarch mixtur expert probabilist model combin classif method fusion detect method simul surveil data waterborn diseas outbreak use research train evalu hierarch mixtur expert model perform evalu approach respect sensitivityspecif tradeoff detect timeli provid comparison sever detect method

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