Unsupervised Anomaly Detection to Characterize Heterogeneity in Type 2 Diabetes

Diabetes is associated with heterogeneous behaviors affecting patients' clinical characteristics and trajectories. This study includes 21,288 patients with type 2 diabetes (women, ages 30 to 65). The cohort was filtered through a set of preprocessing heuristics in order to assure the cohort exhibited a similar clinical trajectory. Anomalous characteristics were then identified using dimensionality reduction and anomaly detection methods. Compared to the majority of the cohort, patients classified as anomalous were twice as likely to be admitted into the hospital (7.94[7.59 8.28] versus 3.12[3.06 3.17] times), have a higher incidence of comorbidities (2[1.64 2.36] times more), and be prescribed more insulin and less new and more expensive diabetes medications (such as Sodium glucose co-transporter 2 inhibitors). Patients with these anomalous characteristics may benefit from additional or specialized interventions to avert their risk for adverse outcomes.