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GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series
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.
Developing moral AI to support antimicrobial decision making
Artificial intelligence (AI) assisting with antimicrobial prescribing raises significant moral questions.
Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation
In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery.
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning
We propose a dual-hormone delivery strategy by exploiting deep reinforcement learning (RL) for people with Type 1 Diabetes (T1D).
Convolutional Recurrent Neural Networks for Glucose Prediction
In addition, the model provides competitive performance in providing effective prediction horizon ($PH_{eff}$) with minimal time lag both in a simulated patient dataset ($PH_{eff}$ = 29. 0$\pm$0. 7 for 30-min and $PH_{eff}$ = 49. 8$\pm$2. 9 for 60-min) and in a real patient dataset ($PH_{eff}$ = 19. 3$\pm$3. 1 for 30-min and $PH_{eff}$ = 29. 3$\pm$9. 4 for 60-min).
