Algorithmic identification of atypical diabetes in electronic health record (EHR) systems.

Aims: Understanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review.

Methods: Patients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data.