Diabetes is a chronic metabolic disorder that affects an estimated 463 million people
worldwide. Aiming to improve the treatment of people with diabetes, digital health has been …

(1) Background: the use of machine learning techniques for the purpose of anticipating
hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in …

Blood glucose (BG) prediction is essential to the success of glycemic control in type 1
diabetes (T1D) management. Empowered by the recent development of the Internet of …

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 …

Abstract Electronic Health Records (EHRs) have become one of the main sources of
evidence to evaluate clinical actions, improve medical quality, predict disease-risk, and …

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 …

Objective To evaluate and understand pregnant patients' perspectives on the
implementation of artificial intelligence (AI) in clinical care with a focus on opportunities to …

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 …

Real-time blood glucose (BG) prediction can enhance decision support systems for insulin
dosing such as bolus calculators and closed-loop systems for insulin delivery. Deep …

Mealtime insulin dosing is a major challenge for people living with type 1 diabetes (T1D).
This task is typically performed using a standard formula that, despite containing some …