arXiv:2607. 02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making.
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.
arXiv:2610.22930v1 Announce Type: cross Abstract: Artificial-intelligence (AI) policies are increasingly used to make consequential decisions in medicine, finance, education, and public services, yet...
Policy learning aims to determine who should be treated based on individual characteristics. In high-stakes settings such as medicine and public policy where safety is a central concern, improving the...
arXiv:2607. 05620v1 Announce Type: cross Abstract: In many decision-making settings, new interventions are acceptable only if they do not reduce outcomes below some established threshold.
arXiv:2606. 01051v1 Announce Type: new Abstract: Dynamic medical treatment requires deciding treatment intensity and intervention timing, while patient states evolve continuously and adverse events may occur between clinical interactions.
arXiv:2609.17296v1 Announce Type: cross Abstract: Policy learning aims to determine who should be treated based on individual characteristics. In high-stakes settings such as medicine and public poli...
arXiv:2606. 17405v1 Announce Type: new Abstract: Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints.
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:2607. 08793v1 Announce Type: cross Abstract: Sepsis is a leading cause of mortality, yet optimal treatment policies remain contested.
The paper introduces a robust reinforcement learning framework that learns adaptive treatment policies for patients, accounting for uncertainties in biomarker measurements and disease dynamics. By treating the patient as a hidden context, the authors unify generalization and robustness through robust contextual Markov decision processes. Applied to a simulator of metastatic prostate cancer calibrated to clinical data, the resulting policies outperform existing adaptive therapy methods, achieving robustness with minimal performance loss under accurate biomarkers and expected disease evolution, and Pareto-dominating the prior approach across varied scenarios.
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.