Effective medication management in Parkinson's Disease (PD) is challenging due to heterogeneous disease progression, variable patient response, and medication side effects. While AI models can forecast levodopa equivalent daily dose (LEDD) as a measure of medication needs, standard uncertainty quantification often fails to communicate the reliability of these predictions, treating high and low confidence clinical decisions identically. We introduce CASCADE (Calibrated Adaptive Scaling via Conformal And Distributional Estimation), a novel conformal prediction framework that propagates epistemic uncertainty from a screening classifier to adapt downstream predictions. Unlike standard conformal methods that rely on auxiliary residual regression, we leverage epistemic uncertainty from a primary classification task (identifying whether a medication change is needed) to dynamically scale the prediction intervals of a secondary regression task (predicting how much change). By mapping Venn-Abers multi-probabilistic uncertainty directly to non-conformity scores, our framework achieves continuous risk adaptation. We demonstrate that this ``cascade effect'' produces highly efficient intervals for confident patients (38.9% narrower than standard conformal baselines) while automatically expanding intervals to ensure robust coverage for uncertain cases, bridging the gap between discrete clinical decision-making and continuous dose forecasting in PD.

翻译：帕金森病（PD）中有效的药物管理因疾病进展异质性、患者反应差异性及药物副作用而具有挑战性。尽管人工智能模型可预测左旋多巴等效日剂量（LEDD）作为用药需求的衡量指标，但标准的不确定性量化方法通常无法传递这些预测的可靠性，将高、低置信度的临床决策等同处理。我们提出CASCADE（基于共形与分布估计的校准自适应缩放），一种新颖的共形预测框架，该框架通过传播筛选分类器的认知不确定性来动态调整下游预测。与依赖辅助残差回归的标准共形方法不同，我们利用主要分类任务（识别是否需要调整用药）的认知不确定性，动态缩放次要回归任务（预测调整幅度）的预测区间。通过将Venn-Abers多概率不确定性直接映射到非一致性分数，我们的框架实现了连续风险自适应。我们证明这种“级联效应”可为高置信患者生成高度紧凑的预测区间（比标准共形基线窄38.9%），同时自动扩展区间以确保对不确定病例的稳健覆盖，从而弥合帕金森病中离散临床决策与连续剂量预测之间的鸿沟。