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• W2996770110 abstract "Providing decision support for questions such as Which Patient to Treat Next? requires a combination of stream-based reasoning and probabilistic reasoning. The former arises due to a multitude of sensors constantly collecting data (data streams). The latter stems from the underlying decision making problem based on a probabilistic model of the scenario at hand. The STARQL engine handles temporal data streams efficiently and the lifted dynamic junction tree algorithm handles temporal probabilistic relational data efficiently. In this paper, we leverage the two approaches and propose probabilistic stream-based reasoning. Additionally, we demonstrate that our proposed solution runs in linear time w.r.t. the maximum number of time steps to allow for real-time decision support and monitoring." @default.
• W2996770110 created "2020-01-10" @default.
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• W2996770110 date "2019-11-01" @default.
• W2996770110 modified "2023-09-23" @default.
• W2996770110 title "Which Patient to Treat Next? Probabilistic Stream-Based Reasoning for Decision Support and Monitoring" @default.
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• W2996770110 doi "https://doi.org/10.1109/icbk.2019.00018" @default.
• W2996770110 hasPublicationYear "2019" @default.
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