AURELIUS: Agentic Uncertainty-Reasoning & Learning with Hierarchical Bayesian Multi-Agents Unified System for Handling Non-Determinism in HealthTech

Abstract

Healthcare AI faces non-determinism owing to noisy clinical signals, incomplete records, and hetero-geneous patient trajectories that evolve over time. These challenges make current systems brittle and prone to overconfident errors when deployed in high-stakes settings such as intensive care or chronic disease management. Objective: To design an agentic AI framework that embeds probabilistic reasoning, uncertainty awareness, and governance mechanisms directly into the decision-making loop for safe, auditable, and interpretable clinical support. Methods: I propose AURELIUS, a hierarchical Bayesian multi-agent architecture that integrates multi-modal uncertainty encoders, graph-based communication, and a meta-agent for option selection under Dec-POMDP formulations. Runtime safety checks, abstention mechanisms, and compliance-aware controllers ensure that policies remain aligned with HIPAA/GDPR constraints. Results: On SepsisSim and MIMIC-IV, AURELIUS improved AUROC by 4.9 pp, reduced calibration error by 31Conclusion: Embedding governance and uncertainty propagation into memory, policy, and coordination yields measurable improvements in safety, interpretability, and efficiency. AURELIUS provides a deployable path toward trustworthy HealthTech AI.

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