GraphRAG in Healthcare

GraphRAG in Healthcare: Enhancing Clinical Reasoning with Knowledge Graphs, GNNs, and Agents

• Giuseppe Futia
• CSI Piemonte
• slides

NoteNotes

• Main topic: The talk explains how graph technologies can support healthcare applications by combining:

  • Knowledge graphs for structured medical knowledge representation.
  • Large language models (LLMs) for extraction, annotation, and reasoning.
  • Graph neural networks (GNNs) for graph-aware embeddings and disambiguation.
  • Agents that can query both public medical knowledge and private patient data.

• Healthcare data challenges:

  • Medical data comes from heterogeneous sources: electronic health records, lab results, diagnoses, medications, clinical notes, reports, publications, and ontologies.
  • Patient data is sensitive, so the system should avoid sending it to external LLM services.
  • The speaker argues for keeping patient data inside local or legacy infrastructure and accessing it virtually when needed.

• Proposed architecture:

  • Use local/open LLMs rather than remote API-based models.
  • Store public medical knowledge in a graph database such as Neo4j.
  • Keep private clinical data in legacy databases and materialize it only at query time.
  • Use graph-based components for ontology integration, information extraction, enrichment, and patient-data access.

• Medical ontologies as semantic infrastructure:

  • The talk highlights resources such as UMLS — Unified Medical Language System — ICD-10, and HPO — Human Phenotype Ontology.
  • UMLS acts as a bridge across medical vocabularies.
  • ICD-10 provides hierarchical disease classifications.
  • HPO connects phenotypic abnormalities, symptoms, diseases, and sometimes frequency information.
  • These ontologies help normalize ambiguous medical terms, such as distinguishing a virus, disease, symptom, or clinical finding.

• Entity recognition and disambiguation:

  • Clinical narratives contain ambiguous terms and synonyms.
  • The system first identifies candidate entities, then disambiguates them using ontology context.
  • Example: “Zika” may refer to multiple related medical entities.
  • Another example distinguishes “shortness of breath,” “dyspnea,” and “tachypnea,” showing that lexical similarity alone is not enough.

• Ontology mapping workflow:

  • Candidate selection is performed using vector similarity over embeddings stored in Neo4j.
  • Candidate disambiguation is then performed with an LLM, using contextual information from ontology structure, definitions, synonyms, and hierarchy.
  • The speaker emphasizes that LLM quality depends heavily on the quality and relevance of the context provided.

• Role of graph neural networks:

  • GNNs are introduced as a way to improve embeddings by incorporating neighborhood structure.

  • The speaker explains message passing through three steps:

    • message,
    • aggregate,
    • update.

  • Instead of representing a node only by its text, a GNN represents it using information from neighboring nodes and relationships.

• Why GNNs help:

  • Pure textual embeddings can miss correct ontology matches when terms are lexically different.
  • GNNs can use relational structure to recover semantically correct candidates.
  • In the validation example, Qwen embeddings failed to place the correct entity in the top five in 34 out of 368 cases; GNN re-ranking rescued about half of those cases.
  • Example: “cervicalgia” should map to “neck pain”; text-only embeddings ranked it 19th, while the GNN-enhanced representation moved it to first place.

• G-Retriever model:

  • The talk introduces a GNN-plus-LLM approach based on G-Retriever.
  • It extracts a relevant subgraph using a Prize-Collecting Steiner Tree-style method.
  • The subgraph is encoded by a GNN and passed to the LLM as graph-derived “soft tokens.”
  • This gives the LLM graph-structured context rather than only textual context.

• Graph agent use case:

  • A graph-based agent can use several tools:

    • query public medical knowledge in the graph,
    • query private patient data virtually,
    • combine both to answer clinical questions.

  • Example questions include retrieving a patient’s follow-up plan or identifying possible diseases based on HPO symptom coverage.

  • The key advantage is that answers are grounded in explicit ontology structure rather than relying only on the LLM’s internal knowledge.

• Core message:

  • Graphs provide a structured, interpretable, and privacy-preserving foundation for healthcare AI.
  • LLMs are useful, but they need well-selected, semantically organized context.
  • GNNs improve retrieval and disambiguation by exploiting graph topology.
  • Agents can unify these components into systems that reason across public medical knowledge and private patient data without unnecessarily moving sensitive data.

• Closing material:

  • The speaker briefly promotes a related book and a knowledge graph training program.
  • The ODSC host closes the event and encourages attendees to revisit sessions on demand and provide feedback.

Reflection