arXiv:2606. 19481v1 Announce Type: new Abstract: Offline reinforcement learning (ORL) offers the potential to improve the quality of clinical decision-making using historical electronic health record (EHR) data.
arXiv:2606. 01028v1 Announce Type: new Abstract: Medical treatment recommendation poses several challenges to reinforcement learning (RL): patient physiology evolves in continuous time, measurements and interventions are performed at irregular intervals, and treatment effects vary substantially across individuals.
The paper introduces MedGym, a benchmark environment for dynamic medical treatment recommendation that models patient evolution in continuous time using Physics-Informed Neural Networks. It addresses gaps in existing reinforcement learning (RL) approaches by allowing evaluation of RL methods under irregular measurement intervals, personalized treatment responses, and safety considerations. MedGym enables direct comparison between discrete-time and continuous-time RL methods and supports clinically relevant metrics such as personalization and trajectory-level safety.
The paper introduces an evidence ladder for evaluating reinforcement learning (RL) in healthcare, outlining stages from problem formulation to lifecycle monitoring. It argues that success in historical data does not guarantee real‑world improvement and highlights assumptions and failure modes at each rung. The authors propose reporting practices to support cumulative evaluation and emphasize that RL should be tested as an intervention within a dynamic sociotechnical system.
arXiv:2605. 19208v2 Announce Type: replace-cross Abstract: Physical activity (PA) plays an important role in maintaining and improving health.
arXiv:2607. 08793v1 Announce Type: cross Abstract: Sepsis is a leading cause of mortality, yet optimal treatment policies remain contested.
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.
EHR2Trace is a system that transforms electronic health records from multiple sources into a standardized, traceable event format suitable for training and evaluating patient world models and clinical agents. It links each event to its original record, separates the event time from the time the information became available, and distinguishes between medication orders, dispensing, and administration. The tool supports both OMOP and MEDS data models, includes automated validation, and was tested on three clinical datasets, converting 846.4 million events and detecting all injected faults.
arXiv:2609.36144v1 Announce Type: new Abstract: Electronic health records provide irregular observations of latent patient states that evolve continuously over time. Recent autoregressive models cond...
The paper proposes a reinforcement learning (RL) fine‑tuning framework for electronic health record (EHR) foundation models, treating them as generative policies over patient trajectories. By framing clinical prediction tasks such as hospital readmission as event‑conditioned, time‑windowed reasoning problems and designing time‑aware, rollout‑sensitive rewards, the authors show that RL fine‑tuning consistently outperforms pre‑trained backbones and strong baselines. The approach enables smaller models to surpass larger pre‑trained models in data‑limited settings, induces positive transfer across tasks, and produces trajectories with stronger structural and semantic alignment to ground truth, improving downstream utility.
arXiv:2606. 29386v1 Announce Type: new Abstract: Predicting a patient's physiological trajectory under a planned treatment sequence is a prospective interventional problem, not standard time-series extrapolation.
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.
The paper introduces CNA, an AI‑oriented comprehensive normalized assessment that transforms vital‑sign distributions into a standard normal space to produce a unified health‑status score. This score serves as a criterion for evaluating and terminating reinforcement learning policies in Renal Replacement Therapy (RRT). Using a 23‑dimensional state representation and matrix decomposition to handle missing data, the authors apply offline RL algorithms and show that the learned strategy reduces mortality from 13.2% to 5.0% and shortens hospital stays by nearly 59 hours compared to physician‑observed treatments.