ALPACA: A Reinforcement Learning Environment for Medication Repurposing and Treatment Optimization in Alzheimer's Disease
arXiv:2602.19298v1 Announce Type: new Abstract: Evaluating personalized, sequential treatment strategies for Alzheimer's disease (AD) using clinical trials is often impractical due to long disease horizons and substantial inter-patient heterogeneity. To address these constraints, we present the Alzheimer's Learning Platform for Adaptive Care Agents (ALPACA), an open-source, Gym-compatible reinforcement learning (RL) environment for systematically exploring personalized treatment strategies using existing therapies. ALPACA is powered by the Continuous Action-conditioned State Transitions (CAST) model trained on longitudinal trajectories from the Alzheimer's Disease Neuroimaging Initiative (ADNI), enabling medication-conditioned simulation of disease progression under alternative treatment decisions. We show that CAST autoregressively generates realistic medication-conditioned trajectories and that RL policies trained in ALPACA outperform no-treatment and behavior-cloned clinician basel
arXiv:2602.19298v1 Announce Type: new Abstract: Evaluating personalized, sequential treatment strategies for Alzheimer's disease (AD) using clinical trials is often impractical due to long disease horizons and substantial inter-patient heterogeneity. To address these constraints, we present the Alzheimer's Learning Platform for Adaptive Care Agents (ALPACA), an open-source, Gym-compatible reinforcement learning (RL) environment for systematically exploring personalized treatment strategies using existing therapies. ALPACA is powered by the Continuous Action-conditioned State Transitions (CAST) model trained on longitudinal trajectories from the Alzheimer's Disease Neuroimaging Initiative (ADNI), enabling medication-conditioned simulation of disease progression under alternative treatment decisions. We show that CAST autoregressively generates realistic medication-conditioned trajectories and that RL policies trained in ALPACA outperform no-treatment and behavior-cloned clinician baselines on memory-related outcomes. Interpretability analyses further indicated that the learned policies relied on clinically meaningful patient features when selecting actions. Overall, ALPACA provides a reusable in silico testbed for studying individualized sequential treatment decision-making for AD.
Executive Summary
The ALPACA environment utilizes reinforcement learning to optimize treatment strategies for Alzheimer's disease, addressing the limitations of clinical trials. By leveraging the Continuous Action-conditioned State Transitions model and data from the Alzheimer's Disease Neuroimaging Initiative, ALPACA generates realistic patient trajectories and enables the development of personalized treatment policies. These policies have been shown to outperform baseline approaches, demonstrating the potential of ALPACA as a valuable tool for improving patient outcomes.
Key Points
- ▸ ALPACA is an open-source reinforcement learning environment for Alzheimer's disease treatment optimization
- ▸ The environment utilizes the Continuous Action-conditioned State Transitions model to simulate disease progression
- ▸ ALPACA enables the development of personalized treatment policies that outperform baseline approaches
Merits
Personalized Treatment
ALPACA allows for the development of personalized treatment strategies, which can lead to improved patient outcomes and more effective disease management.
Demerits
Data Quality and Availability
The performance of ALPACA is dependent on the quality and availability of data, which can be a limitation in certain contexts.
Expert Commentary
The ALPACA environment represents a significant advancement in the application of reinforcement learning to healthcare, particularly in the context of Alzheimer's disease. By providing a framework for the development of personalized treatment strategies, ALPACA has the potential to improve patient outcomes and reduce the burden of disease. However, further research is needed to fully realize the potential of this technology and to address the challenges associated with data quality and availability.
Recommendations
- ✓ Further research should be conducted to validate the performance of ALPACA in diverse patient populations
- ✓ Efforts should be made to integrate ALPACA with existing healthcare systems and clinical workflows