GENERIC PROBABILISTIC NETWORKS IN MEDICAL DECISION SUPPORT

Causal probabilistic networks provide a natural framework for representation of medical knowledge, allowing clinical experts to encode assumptions about dependencies between stochastic variables. Application in medical decision support has produced promising results. However, model parameters may vary between patient groups or over time. Therefore methods are needed that allow for easy calibration of the model to a change in conditions. A solution to this problem is presented and illustrated with an example from a medical decision support system.