AIMC Topic: Blood Glucose Self-Monitoring
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Improving blood glucose level predictability using machine learning.
Diabetes/metabolism research and reviews
Jun 14, 2020
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...
Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
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...
An Artificial Neural Network-based Predictive Model to Support Optimization of Inpatient Glycemic Control.
Diabetes technology & therapeutics
Feb 6, 2020
Achieving glycemic control in critical care patients is of paramount importance, and has been linked to reductions in mortality, intensive care unit (ICU) length of stay, and morbidities such as infection. The myriad of illnesses and patient conditi...
Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG.
Scientific reports
Jan 13, 2020
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patien...
Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning.
PloS one
Dec 2, 2019
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...
A machine-learning approach to predict postprandial hypoglycemia.
BMC medical informatics and decision making
Nov 6, 2019
BACKGROUND: For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies...
GluNet: A Deep Learning Framework for Accurate Glucose Forecasting.
IEEE journal of biomedical and health informatics
Jul 29, 2019
For people with Type 1 diabetes (T1D), forecasting of blood glucose (BG) can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glu...
Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.
Artificial intelligence in medicine
Jul 26, 2019
BACKGROUND: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term health complications and even death if not properly managed. Currently, there is no cure for diabet...
Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques.
Computer methods and programs in biomedicine
Jun 27, 2019
BACKGROUND: Diabetic patients treated with intensive insulin therapies require a tight glycemic control and may benefit from advanced tools to predict blood glucose (BG) concentration levels and hypo/hyperglycemia events. Prediction systems using mac...
Male
Middle Aged
Blood Glucose
Insulin
Diabetes Mellitus, Type 1
Hypoglycemic Agents
Bayes Theorem
Hypoglycemia
Female
Reproducibility of Results
Adult
Sensitivity and Specificity
Machine Learning
Algorithms
Computer Simulation
Retrospective Studies
Humans
Insulin Infusion Systems
Postprandial Period
Capillaries
Blood Glucose Self-Monitoring
False Positive Reactions
Normal Distribution
How Knowledge Emerges From Artificial Intelligence Algorithm and Data Visualization for Diabetes Management.
Journal of diabetes science and technology
May 21, 2019
BACKGROUND: Self-monitoring blood glucose (SMBG) is facilitated by application available to analyze these data. They are mainly based on descriptive statistical analyses. In this study, we are proposing a method inspired by artificial intelligence al...
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