Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
C4.5: programs for machine learning
C4.5: programs for machine learning
An algorithm for finding minimum d-separating sets in belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Due to the uncertain nature of many of the factors that influence on the performance of an emergency medical service, we propose using Bayesian networks to model this kind of systems. We use an algorithm for learning Bayesian networks to build the model, from the point of view of a hospital manager, and apply it to the specific case of a spanish hospital. We also report the results of some preliminary experimentation with the model.