AIMC Topic: Diabetes Mellitus, Type 1

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Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors.

Sensors (Basel, Switzerland)
Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1-5 min, the current blood glucose concentration and its rate-of-change, two key pieces of info...

Improving blood glucose level predictability using machine learning.

Diabetes/metabolism research and reviews
This study was designed to improve blood glucose level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient-specific supervised-machine-learning (SML) analysis of glucose level based on a continuous-glucose-mo...

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes.

Sensors (Basel, Switzerland)
Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insu...

An artificial intelligence decision support system for the management of type 1 diabetes.

Nature metabolism
Type 1 diabetes (T1D) is characterized by pancreatic beta cell dysfunction and insulin depletion. Over 40% of people with T1D manage their glucose through multiple injections of long-acting basal and short-acting bolus insulin, so-called multiple dai...

Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, which commonly goes undetected. Continuous glucose monitoring (CGM) devices have enabled prediction of impending nocturnal hypoglycemia, however, prior efforts have been li...

Glucose outcomes of a learning-type artificial pancreas with an unannounced meal in type 1 diabetes.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Glycemic control with unannounced meals is the major challenge for artificial pancreas. In this study, we described the performance and safety of learning-type model predictive control (L-MPC) for artificial pancreas challe...

A Deep Neural Network Application for Improved Prediction of [Formula: see text] in Type 1 Diabetes.

IEEE journal of biomedical and health informatics
[Formula: see text] is a primary marker of long-term average blood glucose, which is an essential measure of successful control in type 1 diabetes. Previous studies have shown that [Formula: see text] estimates can be obtained from 5-12 weeks of dail...

Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning.

PloS one
Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g. hy...