提升医疗场景下脑电图癫痫分类的不确定性估计可靠性
Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification
- 采用个性化校准策略应对患者分布偏移问题
- 覆盖率提升超20个百分点，预测集大小基本不变
- 适合关注临床决策安全性的医疗AI研究者
在高风险诊断任务中，量化临床预测的不确定性至关重要。共形预测通过提供具有理论覆盖保证的预测集合，提供了严谨的方法。然而，在实际医疗场景中，患者分布偏移违背了标准共形方法所依赖的独立同分布假设，导致覆盖效果不佳。本文在存在已知分布偏移和标签不确定性的脑电图癫痫分类任务上，评估了几种共形预测方法。结果表明，个性化校准策略可使覆盖率提升超过20个百分点，同时保持相近的预测集大小。相关代码已开源，可通过PyHealth框架获取：https://github.com/sunlabuiuc/PyHealth。
原文摘要 · Abstract (English)
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.
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