arXiv:2606. 04632v1 Announce Type: new Abstract: Mechanical ventilation for Acute Respiratory Distress Syndrome (ARDS) requires balancing competing physiological goals, including oxygenation, lung protection, and acid-base homeostasis.
By Teqi Hao, Yuxuan Fu, Xiaoyu Tan, Shaojie Shi, Bohao Lv, Yinghui Xu, Xihe Qiu
arXiv:2608. 14157v1 Announce Type: new Abstract: Mechanical ventilation is a critical life-support intervention, requiring dynamic adjustments to ventilator settings as a patient's condition evolves.
By Chenran Weng, Joo Seung Lee, Malini Mahendra, Anil Aswani
The paper investigates how the granularity of interventions—single components versus full treatment bundles—affects counterfactual simulations using a clinical world model. By analyzing 945,707 patient-hours from MIMIC-IV, the authors show that interventions are naturally bundled, and editing a single component often represents an unreal scenario. Experiments with Clin‑JEPA on 1,019 ventilation onsets demonstrate that editing the complete bundle produces larger predicted state changes than editing individual settings, indicating that bundle-aware editing better captures treatment sensitivity.
By Fangzhou Wang, Yixuan Yang, Camilla Balzarotti, Rishikesan Kamaleswaran
The dosing of intravenous fluids and vasopressors in sepsis is a sequential decision made under uncertainty and guided largely by clinical judgment, which makes it a natural target for reinforcement learning from historical care. Because a learned policy cannot be trialed on patients, its value must be estimated off-policy, and such estimates can be fragile and optimistic.
arXiv:2608. 16482v1 Announce Type: new Abstract: The dosing of intravenous fluids and vasopressors in sepsis is a sequential decision made under uncertainty and guided largely by clinical judgment, which makes it a natural target for reinforcement learning from historical care.
By Marc P\'erez-Roig, David Fern\'andez-Narro, Carlos S\'aez
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.
By Hangqi Ren, Junyi Liao