Synthetic Generation of Patient Service Utilization Data: A Scalability Study.

To address privacy and ethical issues in using health data for machine learning, we evaluate the scalability of advanced synthetic data generation methods like GANs, VAEs, copulaGAN, and transformer models specifically for patient service utilization data. Our study examines five models on data from a Canadian health authority, focusing on training and generation efficiency, data resemblance, and practical utility. Our findings indicate that statistical models excel in efficiency, while most models produce synthetic data that closely mirrors real data, and is also useful for real-world applications.

Prevalence and co-prevalence of comorbidities among patients with type 2 diabetes mellitus.

Objective: Patients with type 2 diabetes (T2DM) often have multiple comorbidities which may impact the selection of antihyperglycemic therapies. The purpose of this study was to quantify the prevalence and co-prevalence of common comorbidities.

Research Design And Methods: A retrospective study was conducted using the Quintiles Electronic Medical Record database.