[HTML][HTML] Machine learning for diabetes clinical decision support: a review
Type 2 diabetes has recently acquired the status of an epidemic silent killer, though it is non-
communicable. There are two main reasons behind this perception of the disease. First, a …
communicable. There are two main reasons behind this perception of the disease. First, a …
Artificial intelligence and machine learning for improving glycemic control in diabetes: best practices, pitfalls and opportunities
Objective: Artificial intelligence and machine learning are transforming many fields including
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …
Self-supervised contrastive pre-training for time series via time-frequency consistency
Pre-training on time series poses a unique challenge due to the potential mismatch between
pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends …
pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends …
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Deep neural operators can learn nonlinear mappings between infinite-dimensional function
spaces via deep neural networks. As promising surrogate solvers of partial differential …
spaces via deep neural networks. As promising surrogate solvers of partial differential …
[HTML][HTML] Enhancing self-management in type 1 diabetes with wearables and deep learning
People living with type 1 diabetes (T1D) require lifelong self-management to maintain
glucose levels in a safe range. Failure to do so can lead to adverse glycemic events with …
glucose levels in a safe range. Failure to do so can lead to adverse glycemic events with …
Personalized blood glucose prediction for type 1 diabetes using evidential deep learning and meta-learning
The availability of large amounts of data from continuous glucose monitoring (CGM),
together with the latest advances in deep learning techniques, have opened the door to a …
together with the latest advances in deep learning techniques, have opened the door to a …
[HTML][HTML] Blood viscosity in subjects with type 2 diabetes mellitus: roles of hyperglycemia and elevated plasma fibrinogen
The viscosity of blood is an indicator in the understanding and treatment of disease. An
elevated blood viscosity has been demonstrated in patients with Type 2 Diabetes Mellitus …
elevated blood viscosity has been demonstrated in patients with Type 2 Diabetes Mellitus …
[HTML][HTML] Operational prediction of solar flares using a transformer-based framework
Y Abduallah, JTL Wang, H Wang, Y Xu - Scientific reports, 2023 - nature.com
Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields
around solar active regions (ARs) is suddenly released. Solar flares and accompanied …
around solar active regions (ARs) is suddenly released. Solar flares and accompanied …
[HTML][HTML] Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis
K Liu, L Li, Y Ma, J Jiang, Z Liu, Z Ye, S Liu… - JMIR Medical …, 2023 - medinform.jmir.org
Background: Machine learning (ML) models provide more choices to patients with diabetes
mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of …
mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of …
Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes
Innovations in digital health and machine learning are changing the path of clinical health
and care. People from different geographical locations and cultural backgrounds can benefit …
and care. People from different geographical locations and cultural backgrounds can benefit …