Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques.
Journal:
Computer methods and programs in biomedicine
PMID:
31416546
Abstract
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 machine learning techniques have mainly focused on applications for sensor augmented pump (SAP) therapy. In contrast, insulin bolus calculators that rely on BG prediction for multiple daily insulin (MDI) injections for patients under self-monitoring blood glucose (SMBG) are scarce because of insufficient data sources and limited prediction capability of forecasting models.
Authors
Keywords
Adult
Algorithms
Bayes Theorem
Blood Glucose
Blood Glucose Self-Monitoring
Capillaries
Computer Simulation
Diabetes Mellitus, Type 1
False Positive Reactions
Female
Humans
Hypoglycemia
Hypoglycemic Agents
Insulin
Insulin Infusion Systems
Machine Learning
Male
Middle Aged
Normal Distribution
Postprandial Period
Reproducibility of Results
Retrospective Studies
Sensitivity and Specificity