The impact of temporal resampling on the safety of clinical reinforcement learning: A simulated trial

The study examines how temporal resampling (binning) of irregular clinical data affects the safety and performance of offline reinforcement learning (ORL) models in healthcare. By analyzing MIMIC‑IV insulin infusion records, the authors show that binning can reverse causal order and obscure critical events. In simulated trials with virtual type‑1 diabetes patients, ORL models trained on binned data performed up to 60% worse than those trained on unprocessed data, yet retrospective evaluation on resampled data overestimated their returns.

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• agents
• reinforcement-learning
• safety