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Found 4 papers, 0 papers with code
Model Predictive Control (MPC) of an Artificial Pancreas with Data-Driven Learning of Multi-Step-Ahead Blood Glucose Predictors
Instead of identifying an open-loop model of the glucoregulatory system from available data, we propose to directly fit the entire BG prediction over a predefined prediction horizon to be used in the MPC, as a nonlinear function of past input-ouput data and an affine function of future insulin control inputs.
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology
The RL approach also leads to a statistically significant reduction in average daily basal insulin dosage compared to conventional therapy.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Modeling Physical Activity Impact on Glucose Dynamics in People with Type 1 Diabetes for a Fully Automated Artificial Pancreas
In this paper, models of the blood glucose (BG) dynamics in people with Type 1 diabetes (T1D) in response to moderate intensity aerobic activity are derived from physiology-based first principles and system identification experiments.
Neurological Status Classification Using Convolutional Neural Network
In this study we show that a Convolutional Neural Network (CNN) model is able to accuratelydiscriminate between 4 different phases of neurological status in a non-Electroencephalogram(EEG) dataset recorded in an experiment in which subjects are exposed to physical, cognitiveand emotional stress.
