OntoSkill: Ontology-aware Agentic Skill Evolution for EHR Clinical Prediction

Electronic Health Records (EHR) clinical prediction requires integrating heterogeneous observations and their complex temporal and clinical dependencies, while agents driven by Large Language Models (LLMs) provide a promising technical direction. Recent studies increasingly explore the agent skill paradigm for experience-driven capability evolution across medical reasoning tasks. However, extending agent skills to EHR clinical prediction faces two fundamental challenges: (1) clinical semantic fragmentation, where isolated measurements lack explicit mappings and dependencies for coherent patient states, and (2) insufficient structural guidance, where performance feedback alone cannot induce clinically meaningful skill structures. To address these issues, we construct an EHR-oriented ontology that structures fragmented observations into meaningful clinical entities, atomic relations, and reasoning rules. We further propose OntoSkill, an ontology-aware agentic skill evolution framework that autonomously forms and evolves reusable clinical skills from experience. OntoSkill resolves three coupled decisions in skill evolution. (i) Where to form: grounding fragmented longitudinal observations into ontology-grounded patient states. (ii) Which to evolve: prioritizing skills based on accumulated prediction experience. (iii) How to evolve: contrasting successful and failed experiences under ontology rules to localize reasoning deficiencies and refine the selected skill. Comprehensive experiments on multiple EHR datasets demonstrate that OntoSkill consistently outperforms state-of-the-art baselines, validating its effectiveness for EHR clinical prediction.