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Found 8 papers, 1 papers with code
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.
Large Language Model for Mental Health: A Systematic Review
Large language models (LLMs) have received much attention and shown their potential in digital health, while their application in mental health is subject to ongoing debate.
Going faster to see further: GPU-accelerated value iteration and simulation for perishable inventory control using JAX
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 represent the age profile of stock.
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).
A Nonlinear Weighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography
A new iterative image reconstruction algorithm for electrical capacitance tomography (ECT) is proposed that is based on iterative soft thresholding of a total variation penalty and adaptive reweighted compressive sensing.
A Brief Survey of Image Processing Algorithms in Electrical Capacitance Tomography
To study the fundamental physics of complex multiphase flow systems using advanced measurement techniques, especially the electrical capacitance tomography (ECT) approach, this article carries out an initial literature review of the ECT method from a point of view of signal processing and algorithm design.
