Explainable apprenticeship learning for hierarchical clinical decision modeling with multimodal alignment and safety-aware feedback
Abdullah, Muhammad Ateeb Ather, Zulaikha Fatima, Carlos Guzmán Sanchéz-Mejorada, Miguel Jesus Torres-Ruiz, Rolando Quintero Téllez, Magdalena Saldana-Perez · BioData Mining · 2026
Clinical diagnosis involves complex sequential reasoning over heterogeneous patient data, yet most existing medical AI systems are optimized for predictive accuracy rather than interpretable modeling of decision processes. In this work, we propose an Explainable Apprenticeship Learning (EAL) framework that formulates clinical reasoning as a hierarchical policy learning problem derived from expert-annotated diagnostic reasoning traces. The framework leverages structured reasoning sequences collected from 61 clinicians across 30 specialties and incorporates multimodal patient representations with Bayesian uncertainty modeling to capture variability in clinical evidence interpretation. To enhance robustness across rare and complex cases, the framework includes a validated synthetic reasoning augmentation strategy grounded in clinical consistency constraints. We further introduce a policy alignment module that compares inferred reasoning trajectories with expert-derived policy distributions, enabling stepwise deviation detection and safety-aware feedback grounded in standardized medical ontologies such as SNOMED CT and UMLS. The proposed approach is evaluated across multiple dimensions, including reasoning trace fidelity, representation quality, policy alignment, diagnostic performance, interpretability, safety compliance, and generalization under domain shift. On the held-out test set, EAL achieved 0.972 Cohen’s κ for reasoning trace fidelity, 0.975 stepwise correctness, 0.953 Top-1 diagnostic accuracy, 0.978 Top-3 accuracy, and 0.975 safety compliance. In a controlled evaluation involving medical trainees, the framework demonstrated improved diagnostic performance following interaction with the alignment feedback module, increasing accuracy from 0.85 ± 0.04 to 0.975 ± 0.02. External validation on unseen casebooks further confirmed strong generalization with 0.968 stepwise correctness. These results suggest that modeling clinical reasoning as a data-driven hierarchical policy learning problem can improve both interpretability and reliability in medical decision-support systems, while also enabling structured analysis of diagnostic reasoning processes for biomedical data mining applications.