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Patient clusters based on HbA1c trajectories: A step toward individualized medicine in type 2 diabetes
2018
PLoS ONE
Aims To identify clinically meaningful clusters of patients with similar glycated hemoglobin (HbA1c) trajectories among patients with type 2 diabetes. Methods A retrospective cohort study using unsupervised machine learning clustering methodologies to determine clusters of patients with similar longitudinal HbA1c trajectories. Stability of these clusters was assessed and supervised random forest analysis verified the clusters' reproducibility. Clinical relevance of the clusters was assessed
doi:10.1371/journal.pone.0207096
fatcat:ltfbgf4flnhifiddnhdvmwqlbq