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Journal Club: Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial
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…
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. Lectures/Seminars. Target Audience: Data Scientists and Medical Data Scientists.
Monday, July 13, 2026, 1:00 PM – 2:00 PM.
For more info visit washington.zoom.us. Journal Club: Towards scalable screening for the early detection of Parkinson’s disease: validation of an iPad-based eye movement assessment system against a clinical-grade eye tracker
Towards scalable screening for the early detection of Parkinson’s disease: validation of an iPad-based eye movement assessment system against a clinical-grade eye tracker, Abstract:
Early detection and monitoring of Parkinson’s disease (PD) remain challenging, highlighting the need for accessible, cost-effective tools. Saccadic eye movement abnormalities are promising noninvasive biomarkers for PD screening and monitoring. Here, we present an iPad-based system that uses a deep learning algorithm to extract saccade metrics and validate these metrics against the clinical-grade EyeLink 1000 Plus. Twenty-five participants (10 with PD, 15 controls) completed pro-saccade, anti-saccade, memory-guided-saccade, and self-generated-saccade tasks. Relative to the EyeLink, the iPad system achieved average subject-level errors of 2 ms for latency and 0.7∘ for amplitude in pro-, anti-, and memory-guided saccades, and 0.003 s−1 for inter-saccadic rate and 1.6∘ for amplitude in self-generated saccades. A review of 22 studies…
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. Lectures/Seminars. Target Audience: Data Scientists and Medical Data Scientists.
Monday, August 10, 2026, 1:00 PM – 2:00 PM.
For more info visit washington.zoom.us.