GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series

no code implementations • 26 Feb 2024 • Chengzhe Piao, Taiyu Zhu, Stephanie E Baldeweg, Paul Taylor, Pantelis Georgiou, Jiahao Sun, Jun Wang, Kezhi Li

Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with diabetes, thereby reducing complications and improving quality of life.

Graph Attention Management +1

Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation

no code implementations • 18 May 2020 • Taiyu Zhu, Kezhi Li, Pau Herrero, Pantelis Georgiou

In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery.

Q-Learning reinforcement-learning +1

A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning

no code implementations • 9 Oct 2019 • Taiyu Zhu, Kezhi Li, Pantelis Georgiou

We propose a dual-hormone delivery strategy by exploiting deep reinforcement learning (RL) for people with Type 1 Diabetes (T1D).

Q-Learning Reinforcement Learning (RL)