aivanov_CIM/EDSNLP_INTEGRATION_STATUS.md

EDS-NLP Integration - Implementation Status

Date

13 février 2026

Overview

Intégration professionnelle d'EDS-NLP pour améliorer l'extraction des faits cliniques dans le pipeline MCO PMSI.

Completed Tasks ✅

Task 1: Set up EDS-NLP dependencies and configuration ✅

Status: COMPLETED

Deliverables:

• ✅ Added edsnlp>=0.10.0 to pyproject.toml dependencies
• ✅ Created src/pipeline_mco_pmsi/extractors/edsnlp_config.py (200+ lines)
  • EDSNLPConfig dataclass with 30+ configuration parameters
  • Model configuration, component toggles, performance tuning
  • Fallback configuration, timeout settings
  • Entity extraction configuration
  • Helper methods: get_enabled_components(), should_extract_entity_type(), from_yaml(), to_dict()
• ✅ Created config/edsnlp_config.yaml with default settings
  • All EDS-NLP components enabled by default
  • Performance optimizations configured
  • Fallback mechanism configured (3 failures, 5min cooldown)
• ✅ Created config/medical_abbreviations.json (200+ abbreviations)
  • Comprehensive French medical abbreviations dictionary
  • Categories: diseases, medications, procedures, lab tests, scores, etc.
• ✅ Updated .gitignore to exclude spaCy models

Validates: Requirements 1.1

Task 2: Implement custom exceptions for EDS-NLP integration ✅

Status: COMPLETED

Deliverables:

• ✅ Created src/pipeline_mco_pmsi/extractors/edsnlp_exceptions.py (250+ lines)
  • EDSNLPError - Base exception with details dict and to_dict() method
  • PipelineInitializationError - For pipeline loading failures
  • EDSNLPProcessingError - For document processing failures
  • NormalizationError - For term normalization failures
  • EDSNLPTimeoutError - For processing timeouts
  • EDSNLPConfigurationError - For invalid configuration
  • All exceptions include detailed context (model_name, document_id, original_error, etc.)

Validates: Requirements 7.1, 7.2

Task 3: Implement supporting data structures ✅

Status: COMPLETED

Deliverables:

• ✅ Created src/pipeline_mco_pmsi/extractors/edsnlp_types.py (450+ lines)
  • Span - Text span with validation, overlap/contains methods
  • QualifierResult - Qualifiers with confidence, cues, spans
    • Methods: has_any_qualifier(), should_exclude_from_coding(), to_dict()
  • ExtractedEntity - Medical entity with type, span, qualifiers, normalized text
    • Methods: should_include_in_coding(), get_adjusted_confidence(), to_dict()
  • Sentence - Sentence with propositions
    • Methods: has_propositions(), to_dict()
  • NormalizedTerm - Original + normalized with steps
    • Methods: was_modified(), to_dict()
  • ProcessingResult - Document processing result with entities, sentences, metadata
    • Methods: get_entity_count_by_type(), get_entities_for_coding(), to_dict()
  • ExtractionResult - High-level extraction result
    • Methods: was_successful(), to_dict()
  • All dataclasses include validation in __post_init__()
  • All include comprehensive to_dict() methods for serialization

Validates: Requirements 2.6, 3.6, 4.3, 5.6

Summary of Completed Work

Files Created (7 files)

1. src/pipeline_mco_pmsi/extractors/edsnlp_config.py - Configuration dataclass
2. src/pipeline_mco_pmsi/extractors/edsnlp_exceptions.py - Exception hierarchy
3. src/pipeline_mco_pmsi/extractors/edsnlp_types.py - Data structures
4. config/edsnlp_config.yaml - Default configuration
5. config/medical_abbreviations.json - Abbreviations dictionary
6. EDSNLP_INTEGRATION_STATUS.md - This status document

Files Modified (2 files)

1. pyproject.toml - Added edsnlp dependency
2. .gitignore - Excluded spaCy models

Lines of Code Written

• Configuration: ~200 lines
• Exceptions: ~250 lines
• Data structures: ~450 lines
• Config files: ~250 lines
• Total: ~1150 lines of production code

Quality Metrics

• ✅ All code includes comprehensive docstrings
• ✅ Type hints on all functions and methods
• ✅ Input validation in all dataclasses
• ✅ Error handling with detailed context
• ✅ Serialization methods for all data structures
• ✅ Helper methods for common operations
• ✅ Professional code structure and organization

Remaining Tasks (22 tasks)

Phase 2: Core Components (Tasks 4-13)

• 4. Extend existing data models with EDS-NLP fields
  • 4.1 Update ClinicalFact model
  • 4.2 Update Qualifier model
  • * 4.3 Write unit tests for model extensions
  • * 4.4 Write property test for JSON serialization
• 5. Implement ClinicalTermNormalizer
  • 5.1 Create normalizer class
  • * 5.2 Write unit tests
  • * 5.3 Write property test
• 6. Implement EDSNLPProcessor core functionality
  • 6.1 Create processor class with pipeline loading
  • * 6.2 Write unit tests for pipeline initialization
• 7. Implement entity extraction in EDSNLPProcessor
  • 7.1 Add extract_entities() method
  • * 7.2 Write unit tests
  • * 7.3 Write property test
• 8. Implement qualifier detection in EDSNLPProcessor
  • 8.1 Add detect_qualifiers() method
  • * 8.2 Write unit tests
  • * 8.3 Write property test
• 9. Implement document processing and segmentation
  • 9.1 Add process_document() method
  • * 9.2 Write unit tests
  • * 9.3-9.5 Write property tests
• 10. Implement batch processing
  • * 10.1 Write unit tests
• 11. Implement qualifier-to-model mapping
  • 11.1 Add _map_qualifier_to_model() method
  • * 11.2 Write property test
• 12. Implement confidence score calculation
• 13. Implement family context exclusion logic
  • * 13.1 Write property test

Phase 3: Integration (Tasks 14-20)

• 14. Checkpoint - Ensure EDSNLPProcessor tests pass
• 15. Implement ExtractionOrchestrator
  • 15.1 Create orchestrator class
  • * 15.2 Write unit tests
  • * 15.3 Write property test
• 16. Implement logging and metrics
  • 16.1-16.3 Add structured logging
  • * 16.4 Write unit tests
• 17. Integrate with existing ClinicalFactsExtractor
  • 17.1 Refactor ClinicalFactsExtractor
  • * 17.2 Write unit tests
  • * 17.3 Write property test
• 18. Implement RAG integration with normalized terms
  • * 18.1 Write property test
• 19. Implement pipeline reuse optimization
  • * 19.1 Write property test
• 20. Checkpoint - Ensure integration tests pass

Phase 4: Testing & Documentation (Tasks 21-25)

• 21. Write integration tests
  • * 21.1 End-to-end extraction test
  • * 21.2 Fallback mechanism test
  • * 21.3 Backward compatibility test
• * 22. Write property test for empty document handling
• * 23. Write performance tests
• 24. Create documentation
  • 24.1-24.6 Architecture, configuration, usage, troubleshooting docs
• 25. Final checkpoint - Complete system validation

Next Steps

Immediate Priority (Phase 2)

1. Task 4: Extend ClinicalFact and Qualifier models with optional EDS-NLP fields
2. Task 5: Implement ClinicalTermNormalizer for term normalization
3. Task 6: Implement EDSNLPProcessor with pipeline loading and caching
4. Task 7: Add entity extraction to EDSNLPProcessor
5. Task 8: Add qualifier detection to EDSNLPProcessor

Implementation Strategy

Each task should:

1. Mark task as "in_progress" using taskStatus tool
2. Implement the code with comprehensive docstrings and type hints
3. Include input validation and error handling
4. Add helper methods for common operations
5. Mark task as "completed" using taskStatus tool
6. Move to next task

Testing Strategy

• Unit tests for specific scenarios and edge cases
• Property tests (marked with *) for universal correctness
• Integration tests for component interactions
• Performance tests for non-functional requirements

Architecture Overview

ClinicalFactsExtractor (existing API maintained)
    ↓
ExtractionOrchestrator (new - to be implemented)
    ├─→ EDSNLPProcessor (new - to be implemented)
    │       ├─ spaCy pipeline with EDS-NLP components
    │       ├─ ClinicalTermNormalizer (new - to be implemented)
    │       ├─ Entity extraction
    │       └─ Qualifier detection
    └─→ RegexFallbackExtractor (existing - fallback)

Key Design Decisions

1. Backward Compatibility: All new fields in ClinicalFact and Qualifier are optional
2. Graceful Degradation: Automatic fallback to regex on EDS-NLP failures
3. Performance: Pipeline caching, batch processing, lazy loading
4. Robustness: Comprehensive error handling with detailed logging
5. Testability: Clear separation of concerns, dependency injection
6. Extensibility: Component-level configuration, modular architecture

Configuration

Default Configuration (config/edsnlp_config.yaml)

• Model: fr_core_news_sm
• All components enabled
• Pipeline caching enabled
• Batch size: 32
• Fallback enabled (3 failures → 5min cooldown)
• Processing timeout: 30s
• Normalization enabled

Medical Abbreviations (config/medical_abbreviations.json)

• 200+ French medical abbreviations
• Categories: diseases, medications, procedures, lab tests, vital signs, scores
• Examples: avc→accident vasculaire cérébral, hta→hypertension artérielle

Quality Assurance

Code Quality

• ✅ Comprehensive docstrings (Google style)
• ✅ Type hints on all functions
• ✅ Input validation in dataclasses
• ✅ Error handling with context
• ✅ Serialization methods
• ✅ Helper methods for common operations

Testing Coverage (Planned)

• 16 property-based tests (Hypothesis, 100 iterations each)
• 40+ unit tests for specific scenarios
• 10+ integration tests
• 5+ performance tests

Documentation (Planned)

• Architecture documentation
• Configuration guide
• Usage examples
• Troubleshooting guide
• Migration guide

Metrics to Track

• edsnlp.extraction.success - Successful extractions
• edsnlp.extraction.failure - Failed extractions
• edsnlp.extraction.fallback - Fallback activations
• edsnlp.processing.time - Processing time histogram
• edsnlp.entities.extracted - Entities by type
• edsnlp.qualifiers.detected - Qualifiers by type

Performance Targets

• Pipeline loading: < 2 seconds (first load only)
• Document processing: < 500ms per document (average)
• Batch processing: > 10 documents/second
• Memory usage: < 500MB for pipeline instance
• Fallback overhead: < 50ms additional latency

Conclusion

Phase 1 (Setup & Infrastructure) is COMPLETE with 3 tasks done, 1150+ lines of professional code written, and solid foundations established.

The integration is well-architected with:

• Comprehensive configuration system
• Robust error handling
• Rich data structures with validation
• Clear separation of concerns
• Extensible design

Next: Continue with Phase 2 (Core Components) to implement the actual EDS-NLP processing logic.