Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification
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arXiv
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| Format: | Preprint |
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2026
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| author | Chatterjee, Arjun Razin, Sayeed Sajjad Wu, John Laghuvarapu, Siddhartha Pradeepkumar, Jathurshan Sun, Jimeng |
| author_facet | Chatterjee, Arjun Razin, Sayeed Sajjad Wu, John Laghuvarapu, Siddhartha Pradeepkumar, Jathurshan Sun, Jimeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19483 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification Chatterjee, Arjun Razin, Sayeed Sajjad Wu, John Laghuvarapu, Siddhartha Pradeepkumar, Jathurshan Sun, Jimeng Machine Learning Artificial Intelligence 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. |
| title | Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2602.19483 |