# Task 8.1 Summary: ClinicalFactsExtractor Implementation

## Overview
Successfully implemented the `ClinicalFactsExtractor` class for extracting structured clinical facts from medical documents with qualifier detection and evidence association.

## Implementation Details

### Files Created
1. **src/pipeline_mco_pmsi/extractors/__init__.py** - Module initialization
2. **src/pipeline_mco_pmsi/extractors/clinical_facts_extractor.py** - Main extractor implementation
3. **tests/test_clinical_facts_extractor.py** - Comprehensive unit tests

### Key Features Implemented

#### 1. Clinical Facts Extraction (`extract_facts()`)
- Extracts structured facts from clinical documents
- Supports multiple fact types:
  - **Diagnostics**: Diagnoses, conclusions, impressions
  - **Actes**: Medical procedures, interventions, surgeries
  - **Examens**: Tests, imaging, lab results
  - **Traitements**: Medications, prescriptions, therapies
- Associates each fact with precise textual evidence (document_id, span, text)
- Generates unique fact IDs using UUID

#### 2. Qualifier Detection (`detect_qualifiers()`)
- **Negation Detection**: Identifies negated facts using markers:
  - "pas de", "absence de", "sans", "aucun", "ni"
  - "exclu", "infirmé", "non retrouvé"
- **Suspicion Detection**: Identifies suspected/uncertain facts:
  - "possible", "suspecté", "probable", "à confirmer"
  - "évocateur", "compatible avec", "hypothèse"
- **Priority System**: Negation takes priority over suspicion
- **Confidence Adjustment**: Reduces confidence scores based on qualifiers:
  - Negated facts: confidence = 0.3
  - Suspected facts: confidence = 0.6
  - Affirmed facts: confidence = 1.0

#### 3. Temporality Detection (`_detect_temporality()`)
- **Antécédents**: "antécédent", "ancien", "histoire de", "connu pour"
- **Chronique**: "chronique", "persistant", "au long cours", "de longue date"
- **Actuel**: Default temporality when no markers detected

#### 4. Evidence Association
- Each fact includes:
  - `document_id`: Source document identifier
  - `span`: Exact character positions (start, end)
  - `text`: Extracted text
  - `context`: Surrounding text (±50 characters)
- Enables full traceability and auditability

#### 5. Confidence Calculation
- Base confidence from qualifier detection
- Adjusted for temporality:
  - Antécédents: ×0.9
  - Chronique: ×0.95
- Final confidence bounded to [0.0, 1.0]

### Technical Implementation

#### Pattern-Based Extraction
- Uses compiled regex patterns for performance
- Separate patterns for each fact type
- Case-insensitive matching
- Captures both structured (e.g., "Diagnostic: ...") and free-text mentions

#### Context Window Analysis
- 150-character window for qualifier detection
- Handles markers before, within, or after fact text
- Marker relevance check (max 50 characters distance)

#### Marker Relevance Algorithm
- Detects if marker is within the extracted fact text
- Checks proximity for markers before/after the fact
- Case-insensitive matching with fallback to first word

## Test Coverage

### Unit Tests (35 tests, all passing)
1. **Qualifier Detection Tests** (8 tests)
   - Negation with various markers
   - Suspicion detection
   - Affirmation (no markers)
   - Priority handling

2. **Temporality Detection Tests** (6 tests)
   - Antécédent keywords
   - Chronique conditions
   - Default to "actuel"

3. **Fact Extraction Tests** (8 tests)
   - Extraction by fact type
   - Negation handling
   - Suspicion handling
   - Antécédent handling

4. **Stay-Level Extraction Tests** (2 tests)
   - Multi-section extraction
   - Document ID preservation

5. **Confidence Calculation Tests** (5 tests)
   - High confidence for affirmed facts
   - Reduced confidence for suspected/negated
   - Temporality adjustments
   - Bounds checking

6. **Context Extraction Tests** (4 tests)
   - Context window extraction
   - Start/end boundary handling
   - Ellipsis addition

7. **Marker Relevance Tests** (3 tests)
   - Close markers
   - Distant markers
   - Markers after facts

## Requirements Validated

### Exigence 6.2: Extraction de faits structurés ✓
- Extracts diagnostics, actes, examens, traitements
- Structured data with type, text, qualifier, temporality

### Exigence 6.3: Association avec preuves ✓
- Each fact has evidence with document_id and span
- Exact character positions tracked

### Exigence 6.4: Assignation de qualificateurs ✓
- All facts have qualifiers (affirmé/nié/suspecté)
- Markers detected and recorded

### Exigence 2.1: Détection de négation ✓
- Negation markers detected
- Facts marked as "nié"
- Confidence reduced

### Exigence 2.2: Détection de suspicion ✓
- Suspicion markers detected
- Facts marked as "suspecté"
- Confidence reduced

### Exigence 2.3: Détection de temporalité ✓
- Temporality markers detected
- Facts marked with temporality
- Confidence adjusted

## Integration Points

### Input
- `StructuredStay` from `DocumentProcessor`
- Contains segmented sections from clinical documents

### Output
- List of `ClinicalFact` objects
- Each with:
  - Unique ID
  - Type (diagnostic/acte/examen/traitement)
  - Text content
  - Qualifier (certainty, markers, confidence)
  - Temporality (actuel/antécédent/chronique)
  - Evidence (document_id, span, text, context)
  - Overall confidence score

### Next Steps
- Facts will be used by the `Codeur` to propose CIM-10/CCAM codes
- Evidence will support code justification
- Qualifiers will influence code selection (e.g., negated facts not coded)

## Performance Characteristics
- Compiled regex patterns for fast matching
- Single-pass extraction per section
- O(n) complexity where n = document length
- Minimal memory overhead (streaming processing)

## Code Quality
- Type hints throughout
- Comprehensive docstrings
- Immutable data models (Pydantic)
- 100% test pass rate
- Clear separation of concerns

## Example Usage

```python
from pipeline_mco_pmsi.extractors import ClinicalFactsExtractor
from pipeline_mco_pmsi.processors import DocumentProcessor

# Process documents
processor = DocumentProcessor()
structured_stay = processor.process_documents(documents, stay_metadata)

# Extract facts
extractor = ClinicalFactsExtractor()
facts = extractor.extract_facts(structured_stay)

# Analyze facts
for fact in facts:
    print(f"Type: {fact.type}")
    print(f"Text: {fact.text}")
    print(f"Certainty: {fact.qualifier.certainty}")
    print(f"Temporality: {fact.temporality}")
    print(f"Confidence: {fact.confidence}")
    print(f"Evidence: {fact.evidence.document_id} @ {fact.evidence.span}")
```

## Conclusion
Task 8.1 is complete. The `ClinicalFactsExtractor` successfully extracts structured clinical facts with comprehensive qualifier detection and evidence association, meeting all specified requirements.
