The impact of temporal resampling on the safety of clinical reinforcement learning: A simulated trial
The study tested how resampling clinical data into fixed time intervals affects reinforcement learning models used for patient care. Findings showed that temporal binning significantly reduced model performance by up to 60% in simulations, and standard retrospective evaluations provided misleadingly optimistic results, risking deployment of unsafe models.