Transformation of Compartmental Models into Sequences of Causal Probabilistic Networks
Oppel Ulrich G., Hierle Alexander, Janke Lydia, Moser Wolfgang · Studies in health technology and informatics · 1993
Some metabolic processes are described by compartmental models. They are characterized by a cyclic graph and a system. of differential equations. We introduce a general method to transform such a system into a sequence of acyclic causal probabilistic networks (CPN). This method is based on the solution of the system of differential equations and a stochastification procedure. The result of this transformation is a realistic model of the metabolic process which may be simulated and adapted to new evidence very easily using the shell HUGIN. In contrary to that, the adaption of the deterministic process of the compartmental model to new evidence in general is not possible. Furthermore, such a stochastified description of a metabolic process easily may be modified by adding additional nodes for additional effects to the CPN. We apply this method of transforming a compartmental model into a sequence of CPNs to a simple glucose - insulin model.