Poor glycaemic control in patients with type 2 diabetes can be predicted from patient information systems with the help of machine learning
The risk for poor glycaemic control in patients with type 2 diabetes can be predicted with confidence by using machine learning methods, a new study from Finland finds. The most important factors predicting glycaemic control include prior glucose levels, duration of type 2 diabetes, and the patient’s existing anti-diabetic medicines.