Reinforcement learning (RL) has recently shown promise in predicting
Alzheimer's disease (AD) progression due to its unique ability to model domain
knowledge. However, it is not clear which RL algorithms are well-suited for
this task. Furthermore, these methods are not inherently explainable, limiting
their applicability in real-world clinical scenarios. Our work addresses these
two important questions. Using a causal, interpretable model of AD, we first
compare the performance of four contemporary RL algorithms in predicting brain
cognition over 10 years using only baseline (year 0) data. We then apply SHAP
(SHapley Additive exPlanations) to explain the decisions made by each algorithm
in the model. Our approach combines interpretability with explainability to
provide insights into the key factors influencing AD progression, offering both
global and individual, patient-level analysis. Our findings show that only one
of the RL methods is able to satisfactorily model disease progression, but the
post-hoc explanations indicate that all methods fail to properly capture the
importance of amyloid accumulation, one of the pathological hallmarks of
Alzheimer's disease. Our work aims to merge predictive accuracy with
transparency, assisting clinicians and researchers in enhancing disease
progression modeling for informed healthcare decisions. Code is available at
this https URL

使用强化学习算法预测阿尔茨海默病的进展，并研究其可解释性；通过比较多种 RL 算法性能，发现只有一种能够较好地模拟疾病进展，但后续解释分析显示所有方法未能正确捕捉淀粉样蛋白囤积在阿尔茨海默病中的重要性。