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:2307. 12022v3 Announce Type: replace-cross Abstract: Optimizing individualized treatment sequences for patients who weigh multiple, competing outcomes differently poses a challenge for dynamic treatment regime (DTR) methods, which typically assume a single univariate outcome.
The paper introduces POROS, a framework that uses peer‑grounded counterfactual explanations to generate incremental behavioral steps for chronic disease management. POROS builds a directed acyclic graph of patient states where each edge represents a behavior change that peers have successfully achieved and that improves health outcomes. In two diabetes cohorts, POROS reduces the required improvement per step from over 25–30 percentage points to about 5–6, while most multi‑hop paths involve cross‑patient comparisons.
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:2508. 03875v2 Announce Type: replace Abstract: Many sequential decision problems offer qualitatively different ways of influencing the environment: some interventions act immediately, whereas others induce persistent effects that continue to shape future states long after the decision that initiated them.
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:2606. 19092v1 Announce Type: cross Abstract: Chronic disease management relies on regular patient-provider interactions to follow-up on disease progression and control.
arXiv:2608. 03606v1 Announce Type: new Abstract: Clinical development is sequential decision-making under uncertainty, where a sponsor must plan a portfolio of experiments from heterogeneous evidence.
arXiv:2607. 19616v1 Announce Type: new Abstract: Knowledge graphs (KGs) are widely used to inject prior knowledge into reinforcement learning (RL), yet the literature is dominated by single-domain, positive-result method papers, so we lack a systematic account of when KG structure helps an agent, when it is neutral, and when it hurts.
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:2606. 23603v2 Announce Type: replace Abstract: Unhealthy dietary behavior continues to be a persistent public health issue in the United States, exacerbated by recommendation systems that prioritize user preference without considering nutritional health.
arXiv:2607. 16916v1 Announce Type: new Abstract: Bladder cancer treatment requires personalized and adaptive decision-making, particularly for recurrent disease, where treatment effectiveness changes across successive clinical episodes.
arXiv:2607. 08793v1 Announce Type: cross Abstract: Sepsis is a leading cause of mortality, yet optimal treatment policies remain contested.