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Query algebra operations for interval probabilities
- In Proceedings of the Iternational Conference on Database and Expert Systems Applications (DEXA). Prague, Czech Republic
"... Abstract. The groundswell for the `00s is imprecise probabilities. Whether the numbers represent the probable location of a GPS device at its next sounding, the inherent uncertainty of an individual expert's probability prediction, or the range of values derived from the fusion of sensor data, ..."
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Abstract. The groundswell for the `00s is imprecise probabilities. Whether the numbers represent the probable location of a GPS device at its next sounding, the inherent uncertainty of an individual expert's probability prediction, or the range of values derived from the fusion of sensor data, probability intervals became an important way of representing uncertainty. However, until recently, there has been no robust support for storage and management of imprecise probabilities. In this paper, we define the semantics of traditional query algebra operations of selection, projection, Cartesian product and join, as well as an operation of conditionalization, specific to probabilistic databases. We provide efficient methods for computing the results of these operations and show how they conform to probability theory.
A Reinforcement Learning Approach To Obtain Treatment Strategies In Sequential Medical Decision Problems
, 2003
"... A reinforcement learning approach to obtain treatment strategies in sequential medical decision problems ..."
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A reinforcement learning approach to obtain treatment strategies in sequential medical decision problems
An Architecture for Automated Development of Clinical Practice Guidelines . . .
"... This paper introduces a decision-analytic artificial intelligence architecture based on the DynaMoL family of tools [3], which center on a dynamic decision modeling language, to automate clinical practice guideline development in critical care management. The proposed architecture establishes the ba ..."
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This paper introduces a decision-analytic artificial intelligence architecture based on the DynaMoL family of tools [3], which center on a dynamic decision modeling language, to automate clinical practice guideline development in critical care management. The proposed architecture establishes the base "engine" that supports modeling of the underlying pathophysiological processes, integrating relevant information from multiple sources, manipulating the uncertainties and preferences involved, updating and maintaining the medical and procedural content, and presenting multiple -level recommendations. The output produced can then be integrated or translated into other computerized guideline representation formats for evaluation and implementation
