Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning - Mayo, Michael; Chepulis, Lynne; Paul, Ryan G. (PloS one, 2019)
Keywords: Accuracy; Artificial intelligence; Benchmarking; Biology and Life Sciences; Blood; Blood glucose; Blood Glucose - analysis; Blood Glucose Self-Monitoring - instrumentation; Blood Glucose Self-Monitoring - methods; Business performance management; Computer and Information Sciences; Datasets; Datasets as Topic; Diabetes; Diabetes mellitus; Diabetes mellitus (insulin dependent); Diabetes Mellitus, Type 1 - blood; Diabetes Mellitus, Type 1 - drug therapy; Engineering and Technology; Forecasting; Generalized linear models; Glucose; Humans; Hyperglycemia; Hypoglycemia; Hypoglycemia - blood; Hypoglycemia - chemically induced; Hypoglycemia - diagnosis; Hypoglycemic Agents - administration & dosage; Hypoglycemic Agents - adverse effects; Insulin; Insulin - administration & dosage; Insulin - adverse effects; Knowledge discovery; Laboratory Critical Values; Learning algorithms; Machine learning; Medical research; Medicine and Health Sciences; Methods; Model accuracy; Multilayer perceptrons; Oversampling; Pancreas; Pancreas, Artificial; Patients; Performance measurement; Physical Sciences; Polynomials; Predictions; Preprocessing; Regression models; Research and Analysis Methods; Self Medication - adverse effects; Support Vector Machine; Support vector machines; Technology; Type 1 diabetes; New Zealand; United States--US; Ohio