Arjun Chatterjee, Sayeed Sajjad Razin, John Wu, Siddhartha Laghuvarapu, Jathurshan Pradeepkumar, Jimeng Sun

Researchers at the University of Illinois and collaborators demonstrate that personalized calibration strategies can significantly improve conformal prediction methods for EEG seizure classification, a high-stakes clinical task. Standard conformal prediction assumes independent and identically distributed data, but patient populations shift over time and across settings, undermining coverage guarantees. The team shows that tailored calibration approaches recover over 20 percentage points of coverage while keeping prediction set sizes manageable, and they release their implementation through PyHealth, an open-source healthcare AI framework.