Patient clusters based on HbA1c trajectories: A step toward individualized medicine in type 2 diabetes.
Tomas KarpatiMaya Leventer-RobertsBecca FeldmanChandra Cohen-StaviItamar RazRan BalicerPublished in: PloS one (2018)
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.