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Beck_Moulton
Beck_Moulton

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Building a Medical-Grade Knowledge Graph: Mapping Drug Interactions with Neo4j and LlamaIndex 🩺💻

#ai #dataengineering #python #neo4j

Ever tried asking a standard RAG (Retrieval-Augmented Generation) pipeline if "Drug A interferes with Drug B" across a corpus of 5,000 unstructured clinical papers? If you're using vanilla vector search, you'll likely get a "maybe" or a hallucinated "yes."

In the world of Medical Data Engineering, proximity in vector space doesn't equal clinical fact. To solve this, we need to move from "searching" to "reasoning." Today, we’re building a Medical-Grade Knowledge Graph using Neo4j, LlamaIndex, and PubMed data to map complex drug-drug interactions (DDI) and clinical trial outcomes.

By the end of this guide, you'll understand how to turn messy medical literature into a structured, queryable brain using GraphRAG and Cypher.

🏗️ The Architecture: From Text to Triples

Traditional databases see rows; vector databases see "vibes." Knowledge Graphs see relationships. Our pipeline transforms unstructured PubMed abstracts into a structured graph of entities (Drugs, Diseases, Trials) and edges (INTERACTS_WITH, TREATS, CONTRAINDICATED).

graph TD
    A[PubMed API / MedQA] -->|Unstructured Text| B(LlamaIndex Property Graph Index)
    B -->|LLM Extraction| C{Entity & Relation Extraction}
    C -->|Nodes & Edges| D[Neo4j Graph Database]
    D -->|Cypher Queries| E[Clinical Decision Support]
    D -->|Graph Traversal| F[Advanced DDI Discovery]
    G[User Query] -->|Natural Language| H[LLM + Cypher Generation]
    H --> D
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🛠️ Prerequisites & Tech Stack

To follow along, you'll need:

  • Neo4j: Graph database (AuraDB or local Docker instance).
  • LlamaIndex: Specifically the PropertyGraphIndex for graph construction.
  • PubMed API: To fetch real-world clinical data.
  • OpenAI GPT-4o: For high-precision entity extraction.

Step 1: Connecting to the Pulse (PubMed API)

First, we need to ingest medical literature. We'll fetch abstracts related to specific clinical trials or drug interactions.

from llama_index.readers.papers import PubMedReader

# Fetching papers related to "Drug Interactions" and "Cardiology"
loader = PubMedReader()
documents = loader.load_data(search_query="drug interaction cardiology", max_results=10)

print(f"Loaded {len(documents)} medical documents.")
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Step 2: Defining the Schema and Extraction Logic

Medical data is sensitive. We can't just let the LLM guess. We define a strict schema for our Knowledge Graph. We want to capture Drug, Disease, and Effect entities.

We use LlamaIndex’s PropertyGraphIndex to handle the heavy lifting of turning sentences into triples (Subject -> Predicate -> Object).

from llama_index.core.indices.property_graph import SchemaLLMPathExtractor
from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore

# Define our Graph Store
graph_store = Neo4jPropertyGraphStore(
    username="neo4j",
    password="your_password",
    url="bolt://localhost:7687"
)

# Define our clinical schema
entities = ["DRUG", "DISEASE", "SIDE_EFFECT", "GENE"]
relations = ["TREATS", "CONTRAINDICATED_WITH", "INTERACTS_WITH", "ASSOCIATED_WITH"]

# Setup the extractor
kg_extractor = SchemaLLMPathExtractor(
    possible_entities=entities,
    possible_relations=relations,
    strict=True # Ensure the LLM follows our clinical ontology
)
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Step 3: Indexing into Neo4j

Now, we process the documents. The LLM will read the PubMed abstracts, identify the drugs and their interactions, and push them into Neo4j as nodes and relationships.

from llama_index.core import PropertyGraphIndex

index = PropertyGraphIndex.from_documents(
    documents,
    property_graph_store=graph_store,
    kg_extractors=[kg_extractor],
    show_progress=True
)
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Step 4: Querying Complex Drug Interactions

Once our graph is built, we can perform complex multi-hop queries that would be impossible with standard SQL or Vector search. For instance: "Find all drugs that treat Hypertension but are contraindicated with Warfarin."

Using Cypher, Neo4j’s query language, this becomes trivial:

MATCH (d1:DRUG {name: "Warfarin"})-[:CONTRAINDICATED_WITH]-(d2:DRUG)
MATCH (d2)-[:TREATS]->(dis:DISEASE {name: "Hypertension"})
RETURN d2.name as Safe_Alternative_Check;
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🚀 Taking it to Production: The "WellAlly" Way

Building a prototype is easy; building a production-ready medical intelligence system is hard. You need to handle data versioning, entity resolution (making sure "Aspirin" and "Acetylsalicylic acid" are the same node), and HIPAA-compliant storage.

For those looking for advanced patterns in medical RAG and production-grade knowledge graph deployments, I highly recommend checking out the engineering deep-dives at WellAlly Blog. They cover how to scale these architectures for real-world clinical environments and integrate with legacy EHR systems.

Step 5: The GraphRAG Advantage

Why not just use a Vector DB? Let’s compare.

If a paper says "Drug A inhibits enzyme X, and enzyme X metabolizes Drug B," a vector search might retrieve that chunk but won't "know" that A affects B. A Knowledge Graph explicitly links them: (Drug A)-[INHIBITS]->(Enzyme X)-[METABOLIZES]->(Drug B).

This is the reasoning chain that saves lives in clinical settings.

Conclusion

We’ve successfully transformed unstructured medical text into a queryable, high-integrity knowledge graph. By combining the natural language prowess of LlamaIndex with the structural rigidity of Neo4j, we’ve created a system that doesn't just "guess" but "knows."

What’s next?

  1. Add Entity Resolution to merge duplicate medical terms.
  2. Implement Temporal Graphs to track how clinical trials evolve over time.
  3. Check out WellAlly's latest technical guides for more insights on building robust AI for healthcare.

Are you building in the MedTech space? Drop a comment below or share your thoughts on the future of GraphRAG! 🥑🚀

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