Safe and sound: artificial intelligence in hazardous applications
Safe and sound: artificial intelligence in hazardous applications
PRISM: Probabilistic Symbolic Model Checker
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
A modular approach for representing and executing clinical guidelines
Artificial Intelligence in Medicine
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In the formal analysis of health-care, there is little work that combines probabilistic and temporal reasoning. On the one hand, there are those that aim to support the clinical thinking process, which is characterised by trade-off decision making taking into account uncertainty and preferences, i.e., the process has a probabilistic and decision-theoretic flavour. On the other hand, the management of care, e.g., guidelines and planning of tasks, is typically modelled symbolically using temporal, non-probabilistic, methods. This paper proposes a new framework for combining temporal reasoning with probabilistic decision making. The framework is instantiated with a guideline modelling language combined with probabilistic pharmokinetics and applied to the treatment of diabetes mellitus type 2.