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Journal Club: Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial
Abstract:
The personalized titration and optimization of insulin regimens for treatment of type 2 diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based reinforcement learning (RL) framework (called RL-DITR), which learns the optimal insulin regimen by analyzing glycemic state rewards through patient model interactions. When evaluated during the development phase for managing hospitalized patients with T2D, RL-DITR achieved superior insulin titration optimization (mean absolute error (MAE) of 1.10 ± 0.03 U) compared to other deep learning models and standard clinical methods. We performed a stepwise clinical validation of the artificial intelligence system from simulation to deployment, demonstrating better performance in glycemic control in inpatients compared to junior and intermediate-level physicians through quantitative (MAE of 1.18 ± 0.09 U) and qualitative metrics from a blinded review. Additionally, we conducted a single-arm, patient-blinded, proof-of-concept feasibility…
Event interval: Single day event. Online Meeting Link: https://washington.zoom.us/j/92158637394?pwd=pi87aK9LVz5Jx7KTPgVq0SX7d2xNIL.1. Campus room: F107, 750 Republican St. Seattle 98109. Accessibility Contact: imds@uw.edu. Event Types: Academics. Target Audience: Data Scientists and Medical Data Scientists.
Monday, February 9, 2026, 1:00 PM – 2:00 PM.