用强化学习模拟阿尔茨海默病个性化治疗，提升记忆功能效果
ALPACA: A Reinforcement Learning Environment for Medication Repurposing and Treatment Optimization in Alzheimer's Disease
- 基于真实患者数据构建可模拟用药效果的强化学习环境
- 训练出的策略在记忆指标上优于不治疗和医生经验策略
- 结果可解释，依赖临床有意义的患者特征做决策
由于阿尔茨海默病病程长、患者差异大，通过临床试验评估个性化连续治疗策略常不现实。为此，我们提出阿尔茨海默病自适应护理智能体学习平台（ALPACA），一个开源、兼容Gym的强化学习环境，用于系统探索现有药物的个性化治疗策略。ALPACA基于在阿尔茨海默病神经影像计划（ADNI）纵向数据上训练的连续动作条件状态转移（CAST）模型，实现药物条件下的疾病进展模拟。实验表明，CAST能自回归生成真实药物条件轨迹，且在ALPACA中训练的强化学习策略在记忆相关指标上优于无治疗和行为克隆医生基线策略。可解释性分析显示，学习到的策略依赖于临床上有意义的患者特征进行决策。总体而言，ALPACA为研究阿尔茨海默病个体化连续治疗决策提供了可复用的虚拟测试平台。
原文摘要 · Abstract (English)
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.
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