Our goal is to develop patient-specific, model-based methods to accurately predict potential treatment outcomes and to guide therapy and improve care. Inter- and intra-patient variability is the largest challenge in the glycaemic treatment of insulin insufficient intensive care patients. Although the stochastic model of insulin sensitivity developed by UOC may be used for patient state prediction, recent results suggest that the method can be improved using Stochastic Differential Equation (SDE) models and the 3D stochastic modeling approach. This research will significantly extend this work by using stochastic modeling and developing novel patient state prediction methods to eliminate hypoglycemia and enhance performance and thus outcomes. UOC and BME will create and validate new stochastic models and the corresponding treatment methods. The models and methods will be first validated in the in-silico validation environment. This environment will be updated by UOC and BME according to the new models and treatment methods. The final version of the treatment methods will be integrated into the current ICU protocols by ULG and BME Finally, the treatment methods will be evaluated by clinical validation trials with BMKK and translation for commercialization with EVO.
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 872488.
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