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[2602.19483] Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification
Summary
This article explores the application of conformal prediction methods in healthcare, specifically focusing on EEG seizure classification. It highlights how personalized calibration strategies can enhance prediction accuracy in the face of distribution shifts.
Why It Matters
In healthcare, accurate predictions are crucial for patient outcomes. This study addresses the limitations of traditional conformal prediction methods under real-world conditions, offering solutions that could significantly improve diagnostic reliability and patient safety.
Key Takeaways
- Conformal prediction can provide reliable uncertainty quantification in clinical settings.
- Standard methods often fail due to distribution shifts in patient data.
- Personalized calibration strategies can improve prediction coverage significantly.
- The study focuses on EEG seizure classification, a critical area in healthcare.
- Implementation is available through the open-source PyHealth framework.
Computer Science > Machine Learning arXiv:2602.19483 (cs) [Submitted on 23 Feb 2026] Title:Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification Authors:Arjun Chatterjee, Sayeed Sajjad Razin, John Wu, Siddhartha Laghuvarapu, Jathurshan Pradeepkumar, Jimeng Sun View a PDF of the paper titled Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification, by Arjun Chatterjee and 5 other authors View PDF HTML (experimental) Abstract: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: this https URL. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) Cite as: arXiv:2602.19483 [cs.LG] (or arXiv:2602.19483v1 [cs.LG] for this vers...
