Recent trends and techniques of blood glucose level prediction for diabetes control
Diabetes, a metabolic disorder disease, can cause short-term acute or even long-term
chronic complications in a patient's body. In 2021, 10.5% of the world's adult population had …
chronic complications in a patient's body. In 2021, 10.5% of the world's adult population had …
[HTML][HTML] A meta-learning approach to personalized blood glucose prediction in type 1 diabetes
Accurate blood glucose prediction is a critical element in modern artificial pancreas systems.
Recently, many deep learning-based models have been proposed for glucose prediction …
Recently, many deep learning-based models have been proposed for glucose prediction …
Temporal deep learning framework for retinopathy prediction in patients with type 1 diabetes
The adoption of electronic health records in hospitals has ensured the availability of large
datasets that can be used to predict medical complications. The trajectories of patients in …
datasets that can be used to predict medical complications. The trajectories of patients in …
Deep spatio-temporal wind power forecasting
J Li, M Armandpour - ICASSP 2022-2022 IEEE International …, 2022 - ieeexplore.ieee.org
Wind power forecasting has drawn increasing attention among researchers as the
consumption of renewable energy grows. In this paper, we develop a deep learning …
consumption of renewable energy grows. In this paper, we develop a deep learning …
A stacked long short-term memory approach for predictive blood glucose monitoring in women with gestational diabetes mellitus
Gestational diabetes mellitus (GDM) is a subtype of diabetes that develops during
pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce …
pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce …
Gluformer: Transformer-based Personalized glucose Forecasting with uncertainty quantification
R Sergazinov, M Armandpour… - ICASSP 2023-2023 …, 2023 - ieeexplore.ieee.org
Deep learning models achieve state-of-the art results in predicting blood glucose
trajectories, with a wide range of architectures being proposed. However, the adaptation of …
trajectories, with a wide range of architectures being proposed. However, the adaptation of …
A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Continuous Glucose Monitoring Data
F Giammarino, R Senanayake… - Journal of Diabetes …, 2024 - journals.sagepub.com
Background: Remote patient monitoring (RPM) programs augment type 1 diabetes (T1D)
care based on retrospective continuous glucose monitoring (CGM) data. Few methods are …
care based on retrospective continuous glucose monitoring (CGM) data. Few methods are …
Non-invasive Blood Glucose Detection System with Infrared Pulse Sensor and Hybrid Feature Neural Network
The rising prevalence of diabetes increases the demand for daily blood glucose (BG)
detection, necessitating the urgent development of noninvasive BG detection systems. To …
detection, necessitating the urgent development of noninvasive BG detection systems. To …
Predicting Human Teammate's Workload
MR Giolando, JA Adams - Companion of the 2024 ACM/IEEE …, 2024 - dl.acm.org
High pressure environments (eg, disaster response) can result in variable workload that
decreases human performance, and degrades the overall mission performance of human …
decreases human performance, and degrades the overall mission performance of human …
Features fusion framework for multimodal irregular time-series events
P Tang, X Zhang - Pacific Rim International Conference on Artificial …, 2022 - Springer
Some data from multiple sources can be modeled as multimodal time-series events which
have different sampling frequencies, data compositions, temporal relations and …
have different sampling frequencies, data compositions, temporal relations and …