Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Adaptive Conformal Prediction via Bayesian Uncertainty Weighting for Hierarchical Healthcare Data
Clinical decision-making demands uncertainty quantification that provides both distribution-free coverage guarantees and risk-adaptive precision, requirements that existing methods fail to jointly satisfy. We present a h…
Inductive randomness predictors
This paper introduces inductive randomness predictors, which form a superset of inductive conformal predictors. Its focus is on a very simple special case, binary inductive randomness predictors. It is interesting that b…
Beyond Conformal Predictors: Adaptive Conformal Inference with Confidence Predictors
Conformal prediction (CP) is a robust framework for distribution-free uncertainty quantification, but it requires exchangeable data to ensure valid prediction sets at a user-specified significance level. When this assump…
Computational EfficiencyConformal PredictionPredictionUncertainty Quantification+1Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey
Machine learning methods are increasingly widely used in high-risk settings such as healthcare, transportation, and finance. In these settings, it is important that a model produces calibrated uncertainty to reflect its …
Conformal PredictionDecision MakingSurveyUncertainty QuantificationFair Conformal Predictors for Applications in Medical Imaging
Deep learning has the potential to automate many clinically useful tasks in medical imaging. However translation of deep learning into clinical practice has been hindered by issues such as lack of the transparency and in…
Conformal PredictionDecision MakingDeep LearningLesion Classification+1