Abstract: Method for determining a recommendation value of a control parameter of a fluid infusion device. The method being implemented by a control device and comprising the steps of retrieving user data, feeding a deep reinforcement learning network, outputting a deep reinforcement learning network result, feeding an uncertainty certificates, outputting an uncertainty certificates result, comparing the uncertainty certificates result, determining the recommendation value, of a control parameter of the fluid infusion device based on a state of the unique user using a control algorithm or the deep reinforcement learning network.

Abstract: Method for determining a recommendation value of a control parameter of a fluid infusion device. The method being implemented by a control device and comprising the steps of retrieving user data, feeding a deep reinforcement learning network, outputting a deep reinforcement learning network result, feeding an uncertainty certificates, outputting an uncertainty certificates result, comparing the uncertainty certificates result, determining the recommendation value, of a control parameter of the fluid infusion d.evice based on a state of the unique user using a control algorithm or the deep reinforcement learning network.

Abstract: A control device (30) for determining a recommendation value of a control parameter of a fluid infusion device (20). The control device (30) comprises a retrieving unit (32) configured to retrieve user data. Each data of the user data having a timestamp and the user data being related to a unique user. The user data comprises at least a plurality of amounts of a drug infused to the unique user; a plurality of physiological values of the unique user; and a plurality of estimated values. The control device (30) also comprises a recommendation unit (34). The recommendation unit (34) is configured to determine the recommendation value based at least on a data of the user data and using a reinforcement learning algorithm comprising a plurality of initial reinforcement parameters.

Abstract: A device for determining a predicted value of glycemia, the device including a data processing unit (3) adapted to process time-based data. The data processing unit (3) is configured to implement a temporal predictive model trained to learn a glycemia cyclic temporal behaviour, where said glycemia cyclic temporal behaviour is defined by variations of glycemia values in a cyclic way over a given cyclic period of time. The temporal predictive model is configured to: receive, as inputs: said time-based data; and encoded temporal data, said encoded temporal data being encoded as to represent the given cyclic period of time. The temporal predictive model is also configured to deliver, as output, a predicted value of glycemia.