Background Machine learning (ML) is a subset of Artificial Intelligence (AI) that is used to
predict and potentially prevent adverse patient outcomes. There is increasing interest in the …

Abstract Purpose of Review Glucose management in the hospital is difficult due to non-static
factors such as antihyperglycemic and steroid doses, renal function, infection, surgical …

Importance: Diabetes is common amongst hospitalised patients and contributes to increased
length of stay and poorer outcomes. Digital transformation, particularly the implementation of …

Background: Acute blood glucose (BG) decompensations (hypoglycemia and
hyperglycemia) represent a frequent and significant risk for inpatients and adversely affect …

Objective Machine learning involves the use of algorithms without explicit instructions. Of
late, machine learning models have been widely applied for the prediction of type 2 …

OBJECTIVE We analyzed data from inpatients with diabetes admitted to a large university
hospital to predict the risk of hypoglycemia through the use of machine learning algorithms …

Background Inpatient glucose management can be challenging due to evolving factors that
influence a patient's blood glucose (BG) throughout hospital admission. The purpose of our …

Background: Continuous glucose monitors have shown great promise in improving
outpatient blood glucose (BG) control; however, continuous glucose monitors are not …

Although machine learning models are increasingly being developed for clinical decision
support for patients with type 2 diabetes, the adoption of these models into clinical practice …

Background Diabetes is a medical and economic burden in the United States. In this study, a
machine learning predictive model was developed to predict unplanned medical visits …