Abstract Purpose of Review Glucose management in the hospital is difficult due to non-static
factors such as antihyperglycemic and steroid doses, renal function, infection, surgical …

To avoid the adverse consequences from abrupt increases in blood glucose, diabetic
inpatients should be closely monitored. Using blood glucose data from type 2 diabetes …

We developed an attention model to predict future adverse glycemic events 30 min in
advance based on the observation of past glycemic values over a 35 min period. The …

Precision pharmacotherapy of diabetes requires judicious selection of the optimal
therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding …

People with type 1 diabetes mellitus (T1DM) must continuously monitor their blood glucose
levels and regulate them by insulin dosing to stay in a safe range. A reliable glucose …

Glucose prediction can greatly benefit people with diabetes by allowing them to anticipate
and proactively manage changes in their glucose levels. In this paper, we propose a novel …

From the moment a patient is admitted to the hospital, monitoring begins, and specific
information is collected. The continuous flow of parameters, including clinical and analytical …

Achieving optimal blood glucose control is a complex challenge for individuals with
diabetes, necessitating a delicate balance among insulin dosage, food consumption …

Real-time prediction of blood glucose levels (BGLs) in individuals with type 1 diabetes (T1D)
presents considerable challenges. Accordingly, we present a personalized multitasking …

This paper studies the stability criterion of integral time-varying recurrent neural networks
(RNNs) with zero lower bound and finite-time synchronization based on improved sliding …