Reinforcement-learning optimal control for type-1 diabetes

By Phuong D. Ngo, Susan Wei, Anna Holubová, Jan Muzík, Fred Godtliebsen. 2018

25 citations recorded by the source corpus.

Subfields: Endocrinology, Diabetes and Metabolism, Genetics, Surgery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Endocrinology, Diabetes and Metabolism

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

In Genetics

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu
4. Jenna Wiens

In Surgery

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

Citations in the corpus

• A Reinforcement Learning–Based Method for Management of Type 1 Diabetes: Exploratory Study (2019, positive): “Vrabie et al (2018) and 2 studies by Ngo et al (2018) applied a model-based RL algorithm for controlling blood glucose for type 1 Diabetes [22-24].”
• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “te the glucose-insulin dynamic system in patients. Based on the Palumbo mathematical model [121], the onpolicy SARSA was used for insulin delivery rate [122]. Ngo et al. applied model-based VI method [123] and AC method [124] to reduce the fluctuation of the blood glucose in both fasting and post-mea”
• Control of Blood Glucose for Type-1 Diabetes by Using Reinforcement Learning with Feedforward Algorithm (2018, background): “[16] proposed a reinforcement learning algorithm for updating basal rates in patients with type-1 diabetes.”
• Deep Reinforcement Learning for Closed-Loop Blood Glucose Control (2020, background): “Many of these works investigated the use of RL to adapt existing insulin treatment regimens to learn a human-in-the-loop policy (Ngo et al., 2018; Oroojeni Mohammad Javad et al., 2015; Sun et al., 2018).”