Using hierarchical mixture of experts model for fusion of outbreak detection methods.
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.
In this paper we describe an approach to facilitate the design of Web multimodal interfaces aiming at improving the user experience and the user interface usability using speech recognition together with the usual graphical user interfaces. We present a proposal for usability evaluation based on the heuristic evaluation, which considers the multimodal principles identified during a case study. As a result of using the proposed approach in the case study and from literature review, we report our considerations for the design, development and improvement of the Web multimodal interfaces.