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arXiv Machine Learning By Hangqi Ren, Junyi Liao

Unmasking Toxic Mimicry in Medical Offline Reinforcement Learning for ICU Sepsis Management via Counterfactual Clinical Audits

Read the original on arXiv Machine Learning →

arXiv:2608. 11410v1 Announce Type: new Abstract: Offline reinforcement learning (RL) offers considerable promise for optimizing ICU treatment decisions, yet standard evaluation metrics Mean Squared Error (MSE) and Fitted Q-Evaluation (FQE) assess only behavioral imitation and cannot detect Toxic Mimicry, a failure mode in which agents replicate harmful patterns such as treatment withdrawal during comfort-care transitions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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