A hierarchical conformal framework for uncertainty-aware length of stay prediction in multi-hospital settings.

ABSTRACT: Uncertainty quantification in hierarchical healthcare data presents a fundamental methodological challenge. Existing approaches face fundamental trade-offs. Conformal prediction offers coverage guarantees but struggles to adapt to instance-level difficulty in clusters, while Bayesian methods quantify uncertainty but lose reliability when models are misspecified. We propose a hybrid framework for hierarchical random forests that addresses the trade-off between coverage and precision by combining group-aware conformal calibration with Bayesian posterior uncertainties to yield adaptive prediction intervals with near-nominal empirical coverage. We assess length-of-stay predictions on 61538 patients from 3793 hospitals using 5-fold cross-validation. The hybrid approach achieved near-target cove

SUBMITTER: Shahbazi MA 

PROVIDER: S-EPMC12909939 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature