Clinical simulation using context-sensitive temporal probability models.
We present a language for representing context-sensitive temporal probabilistic knowledge. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language and an implemented algorithm (BNG) that generates Bayesian networks to compute the posterior probabilities of queries. We illustrate the use of the BNG system by applying it to the problem of modeling the effects of medications and other interventions on the condition of a patient in cardiac arrest.
Haddawy
P
P
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee 53201, USA.
Helwig
J W
JW
Ngo
L
L
Krieger
R A
RA
D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D006323:Heart Arrest / Q000503:physiopathology / Q000628:therapy*; D006801:Humans; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer; D011336:Probability