English(EN) The hidden risks of temporal resampling in clinical reinforcement learning

临床强化学习中时间重采样隐藏的风险

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-30 04:00

一篇新论文强调了临床强化学习（RL）中时间重采样相关的重大风险。研究人员发现，将回顾性临床数据处理成均匀的时间间隔（这是离线 RL 的一种常见做法）会将模型性能最多降低 60%。这种分箱方法会创建患者情景的虚构表示，并且回顾性评估可能错误地表明与实际部署相比性能有所提高。 AI

影响临床 RL 中的时间重采样可能导致性能下降和评估失误，对患者护理构成风险。

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arXiv cs.LG TIER_1 English(EN) · Thomas Frost, Hrisheekesh Vaidya, Steve Harris · 2026-04-30 04:00

临床强化学习中时间重采样隐藏的风险

arXiv:2602.06603v3 Announce Type: replace Abstract: Reinforcement learning (RL) is a type of artificial intelligence for making optimal choices. In healthcare, researchers generally use offline RL (ORL), where models are trained and evaluated from retrospective observational data…