aceThe impact of temporal resampling on the safety of clinical reinforcement learning: A simulated trial
Author summary Artificial Intelligence (AI) models could help future doctors by recommending personalised treatments for patients. These models are currently trained and tested using historical patient records rather than real patients. Medical decisions happen at unpredictable intervals, but before training an AI model, researchers usually shift and align the timings of recorded decisions into a regular schedule (‘temporal resampling’). This makes training the AI models easier with standard techniques. We inves…
In a simulated trial, offline reinforcement learning models trained on unedited, naturally timed insulin data outperformed the simulated clinician baseline, and retrospective safety tests predicted their performance accurately.
In a simulated trial with 30 virtual type 1 diabetes patients, resampling decision timings cut AI insulin-dosing performance by up to 60%, and standard retrospective tests predicted 1.5–3x better returns than the models achieved.
The simulated environments have simpler observations and random, state-independent decision intervals, which may limit generalisability to complex real clinical settings, and only model-free algorithms were tested.
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- JournalismPLOS (Public Library of Science)2026-11-06
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Truvace Impact Record TRV-2026-2409, v1: “The impact of temporal resampling on the safety of clinical reinforcement learning: A simulated trial.” Truvace, 2026-11-07. /record/TRV-2026-2409 (accessed on the day you copy it). sha256 6f45f47a518c8e5e…
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