Adaptive Conformal Prediction via Bayesian Uncertainty Weighting for Hierarchical Healthcare Data

• URL: http://arxiv.org/abs/2601.01223v1
• Date: Sat, 03 Jan 2026 16:06:37 GMT
• Title: Adaptive Conformal Prediction via Bayesian Uncertainty Weighting for Hierarchical Healthcare Data
• Authors: Marzieh Amiri Shahbazi, Ali Baheri, Nasibeh Azadeh-Fard,
• Abstract summary: We present a hybrid Bayesian-conformal framework that addresses the fundamental limitation in healthcare predictions.<n>Our approach integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores.<n>We evaluate our method on 61,538 admissions across 3,793 U.S. hospitals and 4 regions.
• Score: 2.922743999325622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 hybrid Bayesian-conformal framework that addresses this fundamental limitation in healthcare predictions. Our approach integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores while maintaining rigorous coverage validity. Evaluated on 61,538 admissions across 3,793 U.S. hospitals and 4 regions, our method achieves target coverage (94.3% vs 95% target) with adaptive precision: 21% narrower intervals for low-uncertainty cases while appropriately widening for high-risk predictions. Critically, we demonstrate that well-calibrated Bayesian uncertainties alone severely under-cover (14.1%), highlighting the necessity of our hybrid approach. This framework enables risk-stratified clinical protocols, efficient resource planning for high-confidence predictions, and conservative allocation with enhanced oversight for uncertain cases, providing uncertainty-aware decision support across diverse healthcare settings.

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