Dynamically generated models for medical decision support systems

Doctors applying mechanical ventilation need to find the best balance between benefit and risk for the patient. Mathematical models simulating patient's reactions to alterations in the ventilation regime may be employed. A framework is introduced that is able to dynamically combine mathematical models from different model families to form a complex interacting model system. Each of these families consists of submodels differing in complexity of dynamics formulation or anatomical/geometrical resolution. The interaction of model systems reveals qualitatively varying results depending on the complexity of the involved models. Realistic overlaying of respiratory and cardiovascular rhythms can be detected in blood gas concentrations.