arXiv：cs.LG· Ricardo Diaz-Rincon, Muxuan Liang, Adolfo Ramirez-Zamora, Benjamin Shickel·· 2 天前AI 评分33
CASCADE 共形预测：面向两阶段临床决策支持的不确定性自适应预测区间
CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support
AI 导读
研究者提出 CASCADE 共形预测框架，将筛选分类器的认知不确定性映射为 Venn-Abers 非一致性分数，动态缩放下游回归任务的预测区间，用于帕金森病左旋多巴等效日剂量（LEDD）预测。该方法对高置信患者生成的区间比标准共形基线窄 38.9%，同时自动扩大不确定病例的区间以保证覆盖。论文已被 ICML 2026 AgenticUQ Workshop 接收。
正文
Abstract: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.
| Comments: | Accepted to ICML 2026 AgenticUQ Workshop. 14 Pages, 3 Figures |
| Subjects: | Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML) |
| Cite as: | arXiv:2605.20468 [cs.LG] |
| (or arXiv:2605.20468v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20468 arXiv-issued DOI via DataCite |
Submission history
From: Ricardo Diaz-Rincon [view email]
[v1]
Tue, 19 May 2026 20:30:10 UTC (669 KB)
[v2]
Wed, 3 Jun 2026 15:45:32 UTC (669 KB)
[v3]
Wed, 30 Sep 2026 23:50:42 UTC (669 KB)
来源：arXiv：cs.LG · arxiv.org