A Multi-LLM Agent System for Modular Ontology Population: A Case Study on ADHD

Attention Deficit Hyperactivity Disorder is a neurodevelopmental disorder whose manifestations vary significantly depending on the context. This situational variability poses major challenges for assessing and understanding symptoms, particularly outside clinical environments. In this work, we propose a framework that integrates a contextual vision to enrich medical information. The framework is composed of three main components: a modular ontology that formalizes both medical and contextual dimensions of ADHD; a multi-agent system powered by large language models for automatically extracting and populating knowledge from heterogeneous data sources; and a clinical rule-based reasoning mechanism capable of inferring high-level interpretations from instantiated data. Experimental results demonstrate the framework’s ability to generate accurate, context-sensitive interpretations of symptom manifestations. This approach lays the groundwork for more personalized, explainable, and context-aware patient monitoring, with promising applications in intelligent healthcare systems.