Personalized tuning of a reinforcement learning control algorithm for glucose regulation

35 citations recorded by the source corpus.

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Reinforcement Learning in Healthcare: A Survey (2019, methodology): “The authors [39] then proposed using estimation of information transfer from insulin to glucose for automatic and personalized tuning of the AC approach.”
Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes (2016, unknown): “Early-stage work in AC algorithms has already been presented, together with preliminary evaluation results [35], [36].”
On-line policy learning and adaptation for real-time personalization of an artificial pancreas (2015, methodology): “Non-parametric models are true data models suitable to work with erratic glucose–insulin dynamics (Daskalaki et al., 2013, 2012), which makes them a powerful tool for adaptive control by reducing the problem of model bias, which frequently occurs when deterministic models are used.”