Jörn Kretschmer

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Potential harmful effects of ventilation therapy could be reduced by model-based predictions of the effects of ventilator settings to the patient. To obtain optimal predictions, the model has to be individualized based on patients' data. Given a nonlinear model, the result of parameter identification using iterative numerical methods depends on initial(More)
Mathematical models can be deployed to simulate physiological processes of the human organism. Exploiting these simulations, reactions of a patient to changes in the therapy regime can be predicted. Based on these predictions, medical decision support systems (MDSS) can help in optimizing medical therapy. An MDSS designed to support mechanical ventilation(More)
Up to now, the impact of electrode positioning on electrical impedance tomography (EIT) had not been systematically analyzed due to the lack of a reference method. The aim of the study was to determine the impact of electrode positioning on EIT imaging in spontaneously breathing subjects at different ventilation levels with our novel lung function(More)
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(More)
The application of mechanical ventilation is a life-saving routine therapy that allows the patient to overcome the physiological impact of surgeries, trauma or critical illness by ensuring vital oxygenation and carbon dioxide removal. Above a certain level of minute ventilation (usually set to ensure acceptable carbon dioxide removal and oxygenation)(More)
Medical Decision Support Systems employ mathematical models to optimize therapy settings. The mathematical models are used to predict patient reactions towards alteration in the therapy regime. This prediction should not be limited to one detail but should feature a broad picture. A previously proposed framework is able to dynamically combine submodels of(More)
A mathematical model of gas exchange is proposed to predict time course of end-tidal CO<inf>2</inf> (etCO<inf>2</inf>) response to alterations in ventilation frequency. The model has been fit to experimental data of patients undergoing general anesthesia. The gas exchange model proposed by Chiari et al. has been extended by a variable dead space compartment(More)
BACKGROUND Successful application of mechanical ventilation as a life-saving therapy implies appropriate ventilator settings. Decision making is based on clinicians' knowledge, but can be enhanced by mathematical models that determine the individual patient state by calculating parameters that are not directly measurable. Evaluation of models may support(More)