Hyperglycaemia in people with and without diabetes admitted to the hospital is associated
with a substantial increase in morbidity, mortality, and health-care costs. Professional …

Background Previous meta-analysis investigating the incidence and prevalence of
hypoglycaemia in both types of diabetes is limited. The purpose of this review is to conduct a …

Artificial intelligence can use real-world data to create models capable of making predictions
and medical diagnosis for diabetes and its complications. The aim of this commentary article …

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 …

Importance Accurate clinical decision support tools are needed to identify patients at risk for
iatrogenic hypoglycemia, a potentially serious adverse event, throughout hospitalization …

Abstract Purpose of Review Hyperglycemia contributes to a significant increase in morbidity,
mortality, and healthcare costs in the hospital. Professional associations recommend insulin …

Background: Machine learning (ML) models provide more choices to patients with diabetes
mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of …

Abstract Purpose of Review The field of inpatient diabetes has advanced significantly over
the last 20 years, leading to the development of personalized treatment approaches …

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 …

Background and objective Hypoglycemia is a key barrier to achieving optimal glycemic
control in people with diabetes, which has been proven to cause a set of deleterious …