Independent research: GraphRAG makes AI agents 80% more truthful | Read the report

NODES 26 — November 12, 2026

Building an Explainable GraphRAG System: From Clinical Notes to Knowledge Graphs with Neo4j

Session track: AI Engineering

Session time:

Session description:

Show how to build a complete GraphRAG pipeline that converts unstructured clinical notes into an explainable Neo4j knowledge graph using LLMs. Cover entity extraction, graph modelling, temporal relationships, Cypher queries, GraphRAG retrieval, and lessons learned. Demonstrate how graph-based reasoning provides transparent, evidence-backed answers and discuss how the same architecture can be applied beyond healthcare.

Speaker

photo of Johan Müllern-Aspegren

Johan Müllern-Aspegren

Enterprise Architect | Emerging Tech Lead @ AIE | AI Futures Lab

Johan Müllern-Aspegren is Emerging Tech Lead at Capgemini’s Applied Innovation Exchange Nordics and Enterprise Architect specializing in AI, knowledge graphs, and agentic systems. He builds GraphRAG solutions and explainable AI applications that combine LLMs with Neo4j, with experience spanning healthcare, innovation, and public-sector transformation.