115 citations · 168 across the 8 of their papers we have counts for
8 papers
Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach
Chengzhe Piao, Taiyu Zhu, Yu Wang +6
Newly diagnosed Type 1 Diabetes (T1D) patients often struggle to obtain effective Blood Glucose (BG) prediction models due to the lack of sufficient BG data from Continuous Glucose…
Large Language Model for Mental Health: A Systematic Review
Zhijun Guo, Alvina Lai, Johan Hilge Thygesen +3
Large language models (LLMs) have attracted significant attention for potential applications in digital health, while their application in mental health is subject to ongoing debat…
GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series
Chengzhe Piao, Taiyu Zhu, Stephanie E Baldeweg +5
Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with diabetes, thereby reducing complications and improving quality…
Going faster to see further: GPU-accelerated value iteration and simulation for perishable inventory control using JAX
Joseph Farrington, Kezhi Li, Wai Keong Wong +1
Value iteration can find the optimal replenishment policy for a perishable inventory problem, but is computationally demanding due to the large state spaces that are required to re…
A novel hand-held interface supporting the self-management of Type 1 diabetes
Robert Spence, Chukwuma Uduku, Kezhi Li +2
The paper describes the interaction design of a hand-held interface supporting the self-management of Type 1 diabetes. It addresses well-established clinical and human-computer int…
Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation
Taiyu Zhu, Kezhi Li, Pau Herrero +1
People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although t…
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning
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). Specifically, double dilated recurrent neural netw…
Convolutional Recurrent Neural Networks for Glucose Prediction
Kezhi Li, John Daniels, Chengyuan Liu +2
Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancr…