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.
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.
The study presents a new sepsis severity score derived from 43 routinely charted variables over a 72‑hour window, trained on 29,116 and 7,691 adult patients from two Massachusetts hospitals. Using mortality as a treatment‑level ranking signal, the index assigns higher scores to non‑survivors (1.19–1.64 points higher on a 0–10 scale) across various baseline strata and shows significant correlations with lactate, MAP, and creatinine changes. Cross‑institutional agreement and external validation demonstrate robust performance, suggesting the score could serve as a decision‑support tool alongside clinical judgment.
arXiv:2607. 08793v1 Announce Type: cross Abstract: Sepsis is a leading cause of mortality, yet optimal treatment policies remain contested.
The paper introduces FAHOC, a hierarchical reinforcement learning framework that models patient preferences by learning high‑level therapeutic options and factored intra‑option policies, while enforcing a cooperation‑aware action masking mechanism. It demonstrates that cooperative patients achieve better health outcomes and that the Q‑function approximation error is bounded. Evaluated on data from ~50,000 comorbid hypertension and type 2 diabetes patients, FAHOC improves quality‑adjusted life years by 0.669, correctly identifies cooperative patients 95.9% of the time, and never violates patient preferences in held‑out tests.
arXiv:2510. 15127v3 Announce Type: replace-cross Abstract: Identifying the effects of mechanical ventilation (MV) protocols in critical care requires analyzing data from heterogeneous patient-ventilator systems in the clinical decision-making environment.
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.
arXiv:2607. 19020v1 Announce Type: cross Abstract: Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolithic blocks, unable to distinguish stable patient physiology from shifting institutional practice.
arXiv:2608. 07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy.
arXiv:2606. 20206v1 Announce Type: cross Abstract: In offline Reinforcement Learning, immediate rewards in logged batch data are often unobserved due to sparse or irregular record-keeping, or censored beyond certain reward values.
arXiv:2608.30442v1 Announce Type: new Abstract: Recent offline reinforcement learning (RL) studies report policies that outperform physician decisions on clinical outcomes. We conduct a systematic, p...
Recent offline reinforcement learning (RL) studies report policies that outperform physician decisions on clinical outcomes. We conduct a systematic, partially crossed evaluation of five offline RL al...