282 lines
11 KiB
Markdown
282 lines
11 KiB
Markdown
# EDS-NLP Integration - Implementation Status
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## Date
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13 février 2026
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## Overview
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Intégration professionnelle d'EDS-NLP pour améliorer l'extraction des faits cliniques dans le pipeline MCO PMSI.
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## Completed Tasks ✅
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### Task 1: Set up EDS-NLP dependencies and configuration ✅
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**Status**: COMPLETED
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**Deliverables**:
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- ✅ Added `edsnlp>=0.10.0` to `pyproject.toml` dependencies
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- ✅ Created `src/pipeline_mco_pmsi/extractors/edsnlp_config.py` (200+ lines)
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- EDSNLPConfig dataclass with 30+ configuration parameters
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- Model configuration, component toggles, performance tuning
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- Fallback configuration, timeout settings
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- Entity extraction configuration
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- Helper methods: `get_enabled_components()`, `should_extract_entity_type()`, `from_yaml()`, `to_dict()`
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- ✅ Created `config/edsnlp_config.yaml` with default settings
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- All EDS-NLP components enabled by default
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- Performance optimizations configured
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- Fallback mechanism configured (3 failures, 5min cooldown)
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- ✅ Created `config/medical_abbreviations.json` (200+ abbreviations)
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- Comprehensive French medical abbreviations dictionary
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- Categories: diseases, medications, procedures, lab tests, scores, etc.
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- ✅ Updated `.gitignore` to exclude spaCy models
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**Validates**: Requirements 1.1
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### Task 2: Implement custom exceptions for EDS-NLP integration ✅
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**Status**: COMPLETED
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**Deliverables**:
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- ✅ Created `src/pipeline_mco_pmsi/extractors/edsnlp_exceptions.py` (250+ lines)
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- `EDSNLPError` - Base exception with details dict and to_dict() method
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- `PipelineInitializationError` - For pipeline loading failures
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- `EDSNLPProcessingError` - For document processing failures
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- `NormalizationError` - For term normalization failures
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- `EDSNLPTimeoutError` - For processing timeouts
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- `EDSNLPConfigurationError` - For invalid configuration
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- All exceptions include detailed context (model_name, document_id, original_error, etc.)
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**Validates**: Requirements 7.1, 7.2
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### Task 3: Implement supporting data structures ✅
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**Status**: COMPLETED
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**Deliverables**:
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- ✅ Created `src/pipeline_mco_pmsi/extractors/edsnlp_types.py` (450+ lines)
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- `Span` - Text span with validation, overlap/contains methods
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- `QualifierResult` - Qualifiers with confidence, cues, spans
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- Methods: `has_any_qualifier()`, `should_exclude_from_coding()`, `to_dict()`
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- `ExtractedEntity` - Medical entity with type, span, qualifiers, normalized text
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- Methods: `should_include_in_coding()`, `get_adjusted_confidence()`, `to_dict()`
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- `Sentence` - Sentence with propositions
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- Methods: `has_propositions()`, `to_dict()`
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- `NormalizedTerm` - Original + normalized with steps
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- Methods: `was_modified()`, `to_dict()`
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- `ProcessingResult` - Document processing result with entities, sentences, metadata
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- Methods: `get_entity_count_by_type()`, `get_entities_for_coding()`, `to_dict()`
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- `ExtractionResult` - High-level extraction result
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- Methods: `was_successful()`, `to_dict()`
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- All dataclasses include validation in `__post_init__()`
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- All include comprehensive `to_dict()` methods for serialization
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**Validates**: Requirements 2.6, 3.6, 4.3, 5.6
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## Summary of Completed Work
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### Files Created (7 files)
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1. `src/pipeline_mco_pmsi/extractors/edsnlp_config.py` - Configuration dataclass
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2. `src/pipeline_mco_pmsi/extractors/edsnlp_exceptions.py` - Exception hierarchy
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3. `src/pipeline_mco_pmsi/extractors/edsnlp_types.py` - Data structures
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4. `config/edsnlp_config.yaml` - Default configuration
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5. `config/medical_abbreviations.json` - Abbreviations dictionary
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6. `EDSNLP_INTEGRATION_STATUS.md` - This status document
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### Files Modified (2 files)
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1. `pyproject.toml` - Added edsnlp dependency
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2. `.gitignore` - Excluded spaCy models
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### Lines of Code Written
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- Configuration: ~200 lines
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- Exceptions: ~250 lines
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- Data structures: ~450 lines
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- Config files: ~250 lines
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- **Total: ~1150 lines of production code**
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### Quality Metrics
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- ✅ All code includes comprehensive docstrings
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- ✅ Type hints on all functions and methods
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- ✅ Input validation in all dataclasses
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- ✅ Error handling with detailed context
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- ✅ Serialization methods for all data structures
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- ✅ Helper methods for common operations
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- ✅ Professional code structure and organization
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## Remaining Tasks (22 tasks)
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### Phase 2: Core Components (Tasks 4-13)
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- [ ] 4. Extend existing data models with EDS-NLP fields
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- [ ] 4.1 Update ClinicalFact model
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- [ ] 4.2 Update Qualifier model
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- [ ]* 4.3 Write unit tests for model extensions
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- [ ]* 4.4 Write property test for JSON serialization
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- [ ] 5. Implement ClinicalTermNormalizer
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- [ ] 5.1 Create normalizer class
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- [ ]* 5.2 Write unit tests
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- [ ]* 5.3 Write property test
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- [ ] 6. Implement EDSNLPProcessor core functionality
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- [ ] 6.1 Create processor class with pipeline loading
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- [ ]* 6.2 Write unit tests for pipeline initialization
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- [ ] 7. Implement entity extraction in EDSNLPProcessor
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- [ ] 7.1 Add extract_entities() method
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- [ ]* 7.2 Write unit tests
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- [ ]* 7.3 Write property test
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- [ ] 8. Implement qualifier detection in EDSNLPProcessor
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- [ ] 8.1 Add detect_qualifiers() method
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- [ ]* 8.2 Write unit tests
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- [ ]* 8.3 Write property test
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- [ ] 9. Implement document processing and segmentation
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- [ ] 9.1 Add process_document() method
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- [ ]* 9.2 Write unit tests
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- [ ]* 9.3-9.5 Write property tests
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- [ ] 10. Implement batch processing
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- [ ]* 10.1 Write unit tests
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- [ ] 11. Implement qualifier-to-model mapping
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- [ ] 11.1 Add _map_qualifier_to_model() method
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- [ ]* 11.2 Write property test
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- [ ] 12. Implement confidence score calculation
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- [ ] 13. Implement family context exclusion logic
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- [ ]* 13.1 Write property test
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### Phase 3: Integration (Tasks 14-20)
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- [ ] 14. Checkpoint - Ensure EDSNLPProcessor tests pass
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- [ ] 15. Implement ExtractionOrchestrator
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- [ ] 15.1 Create orchestrator class
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- [ ]* 15.2 Write unit tests
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- [ ]* 15.3 Write property test
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- [ ] 16. Implement logging and metrics
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- [ ] 16.1-16.3 Add structured logging
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- [ ]* 16.4 Write unit tests
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- [ ] 17. Integrate with existing ClinicalFactsExtractor
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- [ ] 17.1 Refactor ClinicalFactsExtractor
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- [ ]* 17.2 Write unit tests
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- [ ]* 17.3 Write property test
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- [ ] 18. Implement RAG integration with normalized terms
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- [ ]* 18.1 Write property test
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- [ ] 19. Implement pipeline reuse optimization
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- [ ]* 19.1 Write property test
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- [ ] 20. Checkpoint - Ensure integration tests pass
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### Phase 4: Testing & Documentation (Tasks 21-25)
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- [ ] 21. Write integration tests
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- [ ]* 21.1 End-to-end extraction test
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- [ ]* 21.2 Fallback mechanism test
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- [ ]* 21.3 Backward compatibility test
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- [ ]* 22. Write property test for empty document handling
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- [ ]* 23. Write performance tests
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- [ ] 24. Create documentation
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- [ ] 24.1-24.6 Architecture, configuration, usage, troubleshooting docs
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- [ ] 25. Final checkpoint - Complete system validation
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## Next Steps
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### Immediate Priority (Phase 2)
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1. **Task 4**: Extend ClinicalFact and Qualifier models with optional EDS-NLP fields
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2. **Task 5**: Implement ClinicalTermNormalizer for term normalization
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3. **Task 6**: Implement EDSNLPProcessor with pipeline loading and caching
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4. **Task 7**: Add entity extraction to EDSNLPProcessor
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5. **Task 8**: Add qualifier detection to EDSNLPProcessor
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### Implementation Strategy
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Each task should:
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1. Mark task as "in_progress" using taskStatus tool
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2. Implement the code with comprehensive docstrings and type hints
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3. Include input validation and error handling
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4. Add helper methods for common operations
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5. Mark task as "completed" using taskStatus tool
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6. Move to next task
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### Testing Strategy
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- Unit tests for specific scenarios and edge cases
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- Property tests (marked with *) for universal correctness
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- Integration tests for component interactions
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- Performance tests for non-functional requirements
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## Architecture Overview
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```
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ClinicalFactsExtractor (existing API maintained)
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↓
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ExtractionOrchestrator (new - to be implemented)
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├─→ EDSNLPProcessor (new - to be implemented)
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│ ├─ spaCy pipeline with EDS-NLP components
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│ ├─ ClinicalTermNormalizer (new - to be implemented)
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│ ├─ Entity extraction
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│ └─ Qualifier detection
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└─→ RegexFallbackExtractor (existing - fallback)
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```
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## Key Design Decisions
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1. **Backward Compatibility**: All new fields in ClinicalFact and Qualifier are optional
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2. **Graceful Degradation**: Automatic fallback to regex on EDS-NLP failures
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3. **Performance**: Pipeline caching, batch processing, lazy loading
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4. **Robustness**: Comprehensive error handling with detailed logging
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5. **Testability**: Clear separation of concerns, dependency injection
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6. **Extensibility**: Component-level configuration, modular architecture
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## Configuration
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### Default Configuration (config/edsnlp_config.yaml)
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- Model: fr_core_news_sm
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- All components enabled
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- Pipeline caching enabled
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- Batch size: 32
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- Fallback enabled (3 failures → 5min cooldown)
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- Processing timeout: 30s
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- Normalization enabled
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### Medical Abbreviations (config/medical_abbreviations.json)
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- 200+ French medical abbreviations
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- Categories: diseases, medications, procedures, lab tests, vital signs, scores
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- Examples: avc→accident vasculaire cérébral, hta→hypertension artérielle
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## Quality Assurance
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### Code Quality
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- ✅ Comprehensive docstrings (Google style)
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- ✅ Type hints on all functions
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- ✅ Input validation in dataclasses
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- ✅ Error handling with context
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- ✅ Serialization methods
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- ✅ Helper methods for common operations
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### Testing Coverage (Planned)
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- 16 property-based tests (Hypothesis, 100 iterations each)
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- 40+ unit tests for specific scenarios
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- 10+ integration tests
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- 5+ performance tests
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### Documentation (Planned)
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- Architecture documentation
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- Configuration guide
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- Usage examples
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- Troubleshooting guide
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- Migration guide
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## Metrics to Track
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- `edsnlp.extraction.success` - Successful extractions
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- `edsnlp.extraction.failure` - Failed extractions
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- `edsnlp.extraction.fallback` - Fallback activations
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- `edsnlp.processing.time` - Processing time histogram
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- `edsnlp.entities.extracted` - Entities by type
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- `edsnlp.qualifiers.detected` - Qualifiers by type
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## Performance Targets
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- Pipeline loading: < 2 seconds (first load only)
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- Document processing: < 500ms per document (average)
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- Batch processing: > 10 documents/second
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- Memory usage: < 500MB for pipeline instance
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- Fallback overhead: < 50ms additional latency
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## Conclusion
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**Phase 1 (Setup & Infrastructure) is COMPLETE** with 3 tasks done, 1150+ lines of professional code written, and solid foundations established.
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The integration is well-architected with:
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- Comprehensive configuration system
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- Robust error handling
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- Rich data structures with validation
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- Clear separation of concerns
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- Extensible design
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**Next**: Continue with Phase 2 (Core Components) to implement the actual EDS-NLP processing logic.
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