Blood glucose regulation with stochastic optimal control for insulin-dependent diabetic patients

By Saadet Ulas Acikgoz, Urmila M. Diwekar. 2008

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Reinforcement Learning in Healthcare: A Survey (2019, methodology): “sed policy learning algorithms that integrates RL with Gaussian processes to take into account glycemic variability under uncertainty, using the Ito’s stochastic model of the glucose-insulin dynamics [129]. There are also several data-driven studies carried out to analyze RL in diabetes treatment ba”
On-line policy learning and adaptation for real-time personalization of an artificial pancreas (2015, methodology): “The vector P = [ p 1 p 2 p 3 p 4 p 5 ] stands for estimated patient-speciﬁc parameters and in this work the values estimated by Ulas Acikgoz and Diwekar (2010) are used.”