ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2013
Using hierarchical mixture of experts model for fusion of outbreak detection methods.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predicting Clinical Outcomes with Patient Stratification via Deep Mixture Neural Networks.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Using decision fusion methods to improve outbreak detection in disease surveillance.BMC medical informatics and decision making · 2019Article
- Syndromic surveillance: two decades experience of sustainable systems - its people not just data!Epidemiology and infection · 2019Review
- Dengue disease outbreak definitions are implicitly variable.Epidemics · 2015Article
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Authors and funding
4 authors.
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Abstract
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.
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24551367PMC3900208What OpenQuestion holds
Registered trials
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