Medical GraphRAG Assistant

# GraphRAG Implementation Summary **Date**: 2025-11-05 **Status**: Implementation plan complete, ready to execute --- ## What We're Building **Add knowledge graph capabilities to FHIR clinical notes using the rag-templates GraphRAG framework** ### Current Capabilities (from direct_fhir_vector_approach.py) - ✅ Direct access to FHIR native tables (HSFHIR_X0001_R.Rsrc) - ✅ Vector search on clinical notes - ✅ Companion vector table (VectorSearch.FHIRResourceVectors) - ✅ No SQL Builder configuration needed ### New Capabilities with GraphRAG - 🆕 **Medical entity extraction** (symptoms, conditions, medications, procedures) - 🆕 **Relationship mapping** (TREATS, CAUSES, CO_OCCURS_WITH) - 🆕 **Knowledge graph traversal** (find related entities across documents) - 🆕 **Multi-modal search** (vector + text + graph fusion) - 🆕 **Production-ready pipeline** (error handling, connection pooling) --- ## How It Works: BYOT (Bring Your Own Table) Overlay ### Zero-Copy Architecture ``` FHIR Native Table (unchanged) HSFHIR_X0001_R.Rsrc ├─ ResourceString (FHIR JSON) ├─ ResourceType, ResourceId └─ Compartments (patient links) ↓ BYOT overlay (no data migration) rag-templates reads directly from Rsrc table ├─→ Generates vectors → VectorSearch.FHIRResourceVectors └─→ Extracts entities → RAG.Entities + RAG.EntityRelationships ``` **Key insight**: We don't copy FHIR data. GraphRAG reads directly from native tables and adds knowledge graph enrichment alongside. --- ## What You Get ### 1. Medical Entity Extraction From clinical note: *"Patient reports persistent cough and chest pain for 3 days. Prescribed lisinopril 10mg daily."* **Extracted entities**: - `cough` (SYMPTOM) - `chest pain` (SYMPTOM) - `lisinopril` (MEDICATION) - `3 days` (TEMPORAL) ### 2. Relationship Mapping **Relationships**: - `lisinopril` → TREATS → `hypertension` - `cough` → CO_OCCURS_WITH → `chest pain` - `chest pain` → LOCATED_IN → `chest` ### 3. Multi-Modal Search **Query**: "What medications treat respiratory symptoms?" **Search strategy**: - Vector search: Find semantically similar clinical notes - Text search: Keyword match on "medications" and "respiratory" - Graph traversal: Follow TREATS relationships from medications to symptoms - **RRF fusion**: Combine all results with reciprocal rank scoring --- ## Implementation Phases ### Phase 1: BYOT Configuration (30 min) - Create `config/fhir_graphrag_config.yaml` - Map FHIR table columns to rag-templates Document model - Create `fhir_document_adapter.py` to extract clinical notes from FHIR JSON ### Phase 2: GraphRAG Pipeline Setup (45 min) - Initialize GraphRAG pipeline with `create_pipeline('graphrag')` - Load 51 DocumentReference resources from Patient 3 - Run entity extraction (batch processing for 3x speedup) - Populate knowledge graph tables ### Phase 3: Query Interface (30 min) - Create `fhir_graphrag_query.py` for multi-modal search - Test queries like "Has the patient had respiratory issues?" - Validate entity extraction and relationship mapping ### Phase 4: Medical Entity Enhancement (45 min) - Create `medical_entity_extractor.py` with regex patterns - Configure medical-specific entity types - Add medical ontology mapping (optional) **Total estimated time**: 2-3 hours --- ## Success Criteria ✅ **GraphRAG extracts 100+ medical entities** from 51 DocumentReferences ✅ **Knowledge graph contains 200+ relationships** between entities ✅ **Multi-modal queries return accurate results** in < 1 second ✅ **Medical entity types correctly identified** (SYMPTOM, MEDICATION, etc.) --- ## Key Advantages | Aspect | Vector-Only (Current) | GraphRAG (Proposed) | |--------|----------------------|---------------------| | **Entity extraction** | None | ✅ Symptoms, meds, conditions | | **Relationships** | None | ✅ TREATS, CAUSES, CO_OCCURS | | **Search modes** | Vector only | ✅ Vector + Text + Graph | | **Answer quality** | Good | ✅ Excellent (entity-aware) | | **Production features** | Manual | ✅ Connection pooling, error handling | | **Data migration** | None needed | ✅ Still none needed (BYOT) | --- ## Files to Create 1. `/config/fhir_graphrag_config.yaml` - BYOT configuration 2. `fhir_document_adapter.py` - FHIR JSON → Document converter 3. `fhir_graphrag_setup.py` - Pipeline initialization 4. `fhir_graphrag_query.py` - Query interface with demo queries 5. `medical_entity_extractor.py` - Medical entity patterns --- ## Example Usage ### Setup (one-time) ```bash python3 fhir_graphrag_setup.py # Loads 51 documents, extracts entities, builds knowledge graph ``` ### Query ```bash python3 fhir_graphrag_query.py "Has the patient had respiratory issues?" 3 ``` **Output**: ``` 📊 Search Results: - Retrieved: 3 documents - Execution time: 0.847s - Retrieval method: rrf_fusion 🔬 Extracted Entities: - cough (SYMPTOM) - wheezing (SYMPTOM) - chest tightness (SYMPTOM) 🔗 Relationships: - cough → CO_OCCURS_WITH → wheezing - respiratory infection → CAUSES → cough 💡 Answer: Yes, the patient has reported several respiratory issues including persistent cough, wheezing, and difficulty breathing on 2023-03-12. 📝 Sources: 1. DocumentReference/1474 (2023-03-12) 2. DocumentReference/1476 (2023-03-15) ``` --- ## Next Steps 1. **Review implementation plan**: See `GRAPHRAG_IMPLEMENTATION_PLAN.md` 2. **Create config directory**: `mkdir -p config` 3. **Implement Phase 1**: BYOT adapter 4. **Test on existing data**: 51 DocumentReferences from proof of concept 5. **Validate results**: Compare GraphRAG vs. vector-only approach --- ## Questions? - **Does this modify FHIR tables?** No, BYOT reads directly from existing tables - **Do we need to re-run Tutorial 1?** No, we bypass SQL Builder entirely - **Can we use existing vectors?** Yes, GraphRAG can use VectorSearch.FHIRResourceVectors - **What about performance?** Entity extraction adds ~2s per document (one-time), queries < 1s --- **Ready to implement!** See `GRAPHRAG_IMPLEMENTATION_PLAN.md` for detailed technical specifications.