aiqbench analysis
This paper introduces MedGym, a novel benchmark environment designed to evaluate reinforcement learning methods for dynamic medical treatment under continuous-time patient physiology and irregular intervention intervals. By leveraging Physics-Informed Neural Networks, it addresses critical gaps in existing discrete-time frameworks, enabling rigorous comparison of personalization and safety across varying measurement schedules. The work advances the state of machine learning by providing a standardized tool for assessing RL algorithms in complex, real-world clinical scenarios where timing and individual dynamics are paramount.
A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learning
arXiv:2606.01028v2 Announce Type: replace 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. Existing RL formulations and simulated environments, however, are based on discrete-time MDPs with fixed decision intervals. Thus, it remains difficult to evaluate whether RL methods can handle time-interval-dependent disease progression, personalized treatment response, and safety between consecutive measurement points. To address this gap, we introduce MedGym, a benchmark environment for dynamic treatment recommendation. MedGym models longitudinal patient evolution in a continuous-time framework and constructs a configurable medical RL benchmark from clinical data by using Physics-Informed Neural Networks. The resulting benchmark enables direct comparison between discrete-time and continuous-time methods under irregular treatment timing and patient-specific dynamics. Furthermore, MedGym supports evaluation from clinically important perspectives, such as personalization and trajectory-level safety. By providing a standardized and configurable benchmark for continuous-time dynamic treatment, MedGym enables more realistic and informative evaluation of medical RL methods.
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. Existing RL formulations and simulated environments, however, are based on discrete-time MDPs with fixed decision intervals. Thus, it remains difficult to evaluate whether RL methods can handle time-interval-dependent disease progression, personalized treatment response, and safety between consecutive measurement points. To address this gap, we introduce MedGym, a benchmark environment for dynamic treatment recommendation. MedGym models longitudinal patient evolution in a continuous-time framework and constructs a configurable medical RL benchmark from clinical data by using Physics-Informed Neural Networks. The resulting benchmark enables direct comparison between discrete-time and continuous-time methods under irregular treatment timing and patient-specific dynamics. Furthermore, MedGym supports evaluation from clinically important perspectives, such as personalization and trajectory-level safety. By providing a standardized and configurable benchmark for continuous-time dynamic treatment, MedGym enables more realistic and informative evaluation of medical RL methods.
Machine Learning (cs.LG)
arXiv:2606.01028 [cs.LG]
(or arXiv:2606.01028v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.01028 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 254 words
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