用贝叶斯不确定性加权改进医疗预测的置信区间，兼顾覆盖率与精度。
Adaptive Conformal Prediction via Bayesian Uncertainty Weighting for Hierarchical Healthcare Data
- 融合贝叶斯分层随机森林与分组感知校准，用后验不确定性加权符合度得分。
- 在3793家医院数据上实现94.3%覆盖率，低不确定情况区间窄21%。
- 适合需要风险分层决策的临床场景，尤其关注高危病例的稳健性。
临床决策需同时具备分布无关的覆盖保证与风险自适应的精度，现有方法难以兼顾。本文提出一种混合贝叶斯-合规模型，将贝叶斯分层随机森林与分组感知合规模型结合，利用后验不确定性对符合度分数进行加权，同时保持严格的覆盖率。在涵盖61,538次住院、3,793家美国医院及4个区域的数据上，该方法实现94.3%的覆盖率（目标为95%），低不确定性情况下区间宽度缩小21%，高风险预测时适当放宽。关键发现：仅靠校准良好的贝叶斯不确定性会严重欠覆盖（仅14.1%），凸显本方法必要性。该框架支持风险分层临床协议、高置信度预测的资源高效规划，以及不确定案例的保守管理，适用于多样化的医疗环境。
原文摘要 · Abstract (English)
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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