Patient clusters based on HbA1c trajectories: A step toward individualized medicine in type 2 diabetes

Abstract

By applying unsupervised machine learning to longitudinal HbA1c trajectories, we have identified clusters of patients who have distinct risk for diabetes-related complications. These clusters can be the basis for developing individualized models to personalize glycemic targets.