Quantifying uncertainty in clinical predictions is critical for high-stakes diagnosis tasks. Conformal prediction offers a principled approach by providing prediction sets with theoretical coverage guarantees. However, in practice, patient distribution shifts violate the i.i.d. assumptions underlying standard conformal methods, leading to poor coverage in healthcare settings. In this work, we evaluate several conformal prediction approaches on EEG seizure classification, a task with known distribution shift challenges and label uncertainty. We demonstrate that personalized calibration strategies can improve coverage by over 20 percentage points while maintaining comparable prediction set sizes. Our implementation is available via PyHealth, an open-source healthcare AI framework: https://github.com/sunlabuiuc/PyHealth.

翻译：在临床预测中量化不确定性对于高风险诊断任务至关重要。保形预测通过提供具有理论覆盖度保证的预测集，提供了一种原则性方法。然而在实践中，患者分布偏移会违反标准保形方法所基于的独立同分布假设，导致医疗场景中的覆盖度不足。本研究在脑电图癫痫分类任务上评估了多种保形预测方法，该任务存在已知的分布偏移挑战和标签不确定性。我们证明个性化校准策略可将覆盖度提高超过20个百分点，同时保持可比的预测集规模。我们的实现可通过开源医疗人工智能框架PyHealth获取：https://github.com/sunlabuiuc/PyHealth。