@booklet {Abkai2009,
	title = {Physiological Modeling and Real-Time Simulations Based on Dynamic Bayesian Networks},
	howpublished = {MICCAI2009},
	year = {2009},
	abstract = {<p>This paper discusses a new strategy for describing physiological models by Dynamic Bayesian Networks (DBN). These networks allow to mod- el event based dynamic changes of physiological parameters in an independent way, e.g. for medications, interventions, and complications. Hierarchical struc- tures and the graph structures enable knowledge-based modeling and thus to mitigate model complexity. By its statistical nature, the strategy consequently allows handling uncertainty directly. Furthermore, learning routines yield pa- rameter estimation from real data. The applicability is shown for a real-time human patient simulator. Simulation time is dependent to the number of nodes and discrete values, which represent the range of system dynamics. Due to pa- rameter reduction a speed up in comparison to standard integrative approaches is possible. We simulate a circulatory system within 2.5 ms each simulation step on a standard PC.</p>},
	author = {Abkai , Ciamak and Hesser , J{\"u}rgen}
}
