Adaptive Conformal Prediction via Bayesian Uncertainty Weighting for Hierarchical Healthcare Data

2026-01-03

arXiv.org

AI Summary

This study addresses the challenge of achieving both distribution-free coverage guarantees and risk-adaptive predictive accuracy in clinical decision-making—a balance unattained by existing methods. The authors propose a novel framework that integrates Bayesian hierarchical random forests with group-aware conformal calibration, leveraging posterior uncertainty to weight conformity scores. This approach jointly ensures valid coverage and sharp predictions tailored to clinical risk. Evaluated on data from 61,538 hospitalized patients, the method achieves 94.3% coverage (target: 95%), narrows prediction intervals by 21% for low-uncertainty cases, and appropriately widens them for high-risk patients. In contrast, relying solely on Bayesian uncertainty results in severe undercoverage (14.1%). This work thus offers a theoretically grounded and practically accurate solution for risk-stratified clinical decision support.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Decision/Utility Theory
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• uncertainty quantification
• conformal prediction
• Bayesian uncertainty
• clinical decision-making
• coverage guarantee

Innovation

• Bayesian-conformal framework
• hierarchical healthcare data
• uncertainty weighting
• adaptive conformal prediction
• risk-adaptive precision