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test_sejour_15.py

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+#!/usr/bin/env python3
+"""
+Test rapide du séjour 15_23096332
+"""
+
+from pathlib import Path
+from pipeline_mco_pmsi.database.base import get_engine, get_session
+from pipeline_mco_pmsi.database.models import StayDB, ClinicalDocumentDB
+from pipeline_mco_pmsi.models.clinical import ClinicalDocument
+from pipeline_mco_pmsi.models.metadata import StayMetadata
+from pipeline_mco_pmsi.rag.rag_engine import RAGEngine
+from pipeline_mco_pmsi.rag.referentiels_manager import ReferentielsManager
+from pipeline_mco_pmsi.extractors.clinical_facts_extractor import ClinicalFactsExtractor
+from pipeline_mco_pmsi.coders.codeur import Codeur
+
+print("=" * 60)
+print("TEST DU SÉJOUR 15_23096332")
+print("=" * 60)
+
+# 1. Charger le séjour depuis la base
+print("\n1️⃣  Chargement du séjour depuis la base...")
+engine = get_engine('sqlite:///pipeline_mco_pmsi.db')
+
+with get_session(engine) as session:
+    stay = session.query(StayDB).filter(StayDB.stay_id == '15_23096332').first()
+    
+    if not stay:
+        print("❌ Séjour non trouvé")
+        exit(1)
+    
+    print(f"✅ Séjour trouvé: {stay.stay_id}")
+    print(f"   Spécialité: {stay.specialty}")
+    
+    # Récupérer les documents
+    docs_db = session.query(ClinicalDocumentDB).filter(
+        ClinicalDocumentDB.stay_id == stay.id
+    ).order_by(ClinicalDocumentDB.priority).all()
+    
+    print(f"   Documents: {len(docs_db)}")
+    
+    # Créer les métadonnées du séjour (nécessaire pour process_documents)
+    stay_metadata = StayMetadata(
+        stay_id=stay.stay_id,
+        admission_date=stay.admission_date,
+        discharge_date=stay.discharge_date,
+        specialty=stay.specialty
+    )
+    
+    # Convertir en modèles Pydantic
+    documents = []
+    for doc_db in docs_db:
+        doc = ClinicalDocument(
+            document_id=doc_db.document_id,
+            document_type=doc_db.document_type,
+            content=doc_db.content,
+            creation_date=doc_db.creation_date,
+            author=doc_db.author or 'Inconnu',
+            priority=doc_db.priority
+        )
+        documents.append(doc)
+        print(f"     - {doc.document_id}: {doc.document_type} ({len(doc.content)} car.)")
+
+# 2. Initialiser le RAG Engine
+print("\n2️⃣  Initialisation du moteur RAG...")
+referentiels_manager = ReferentielsManager(data_dir=Path('data/referentiels'))
+rag_engine = RAGEngine(referentiels_manager=referentiels_manager)
+print("✅ RAG Engine initialisé")
+
+# 3. Segmentation des documents en sections
+print("\n3️⃣  Segmentation des documents en sections...")
+
+from pipeline_mco_pmsi.processors.document_processor import DocumentProcessor
+
+processor = DocumentProcessor()
+
+try:
+    structured_stay = processor.process_documents(
+        documents=documents,
+        stay_metadata=stay_metadata
+    )
+    print(f"✅ {len(structured_stay.sections)} sections créées")
+    
+    # Afficher quelques sections
+    for i, section in enumerate(structured_stay.sections[:3], 1):
+        print(f"   {i}. {section.section_type}: {section.content[:80]}...")
+    
+    if len(structured_stay.sections) > 3:
+        print(f"   ... et {len(structured_stay.sections) - 3} autres sections")
+
+except Exception as e:
+    print(f"❌ Erreur lors de la segmentation: {e}")
+    import traceback
+    traceback.print_exc()
+    exit(1)
+
+# 4. Extraction des faits cliniques
+print("\n4️⃣  Extraction des faits cliniques...")
+print("   ⏳ Cela peut prendre 1-2 minutes...")
+
+extractor = ClinicalFactsExtractor()
+
+try:
+    facts = extractor.extract_facts(structured_stay)
+    print(f"✅ {len(facts)} faits extraits")
+    
+    # Afficher quelques faits
+    for i, fact in enumerate(facts[:5], 1):
+        print(f"   {i}. [{fact.type}] {fact.text[:80]}...")
+        print(f"      Certitude: {fact.qualifier.certainty}, Temporalité: {fact.temporality}")
+    
+    if len(facts) > 5:
+        print(f"   ... et {len(facts) - 5} autres faits")
+
+except Exception as e:
+    print(f"❌ Erreur lors de l'extraction: {e}")
+    import traceback
+    traceback.print_exc()
+    exit(1)
+
+# 5. Codage
+print("\n5️⃣  Proposition de codes CIM-10 et CCAM...")
+print("   ⏳ Cela peut prendre 2-3 minutes...")
+
+codeur = Codeur(rag_engine=rag_engine)
+
+try:
+    coding_proposal = codeur.propose_codes(
+        facts=facts,
+        stay_metadata=stay_metadata
+    )
+    
+    print("✅ Codage terminé")
+    print(f"\n📊 RÉSULTATS:")
+    print(f"\n  DP (Diagnostic Principal):")
+    if coding_proposal.dp:
+        print(f"    {coding_proposal.dp.code} - {coding_proposal.dp.label}")
+        print(f"    Confiance: {coding_proposal.dp.confidence:.2%}")
+        print(f"    Justification: {coding_proposal.dp.reasoning[:150]}...")
+    else:
+        print("    Non proposé")
+    
+    print(f"\n  DR (Diagnostic Relié):")
+    if coding_proposal.dr:
+        print(f"    {coding_proposal.dr.code} - {coding_proposal.dr.label}")
+        print(f"    Confiance: {coding_proposal.dr.confidence:.2%}")
+    else:
+        print("    Non proposé")
+    
+    print(f"\n  DAS (Diagnostics Associés Significatifs): {len(coding_proposal.das)}")
+    for i, das in enumerate(coding_proposal.das[:5], 1):
+        print(f"    {i}. {das.code} - {das.label}")
+        print(f"       Confiance: {das.confidence:.2%}")
+    
+    if len(coding_proposal.das) > 5:
+        print(f"    ... et {len(coding_proposal.das) - 5} autres DAS")
+    
+    print(f"\n  CCAM (Actes): {len(coding_proposal.ccam)}")
+    for i, ccam in enumerate(coding_proposal.ccam[:5], 1):
+        print(f"    {i}. {ccam.code} - {ccam.label}")
+        print(f"       Confiance: {ccam.confidence:.2%}")
+    
+    if len(coding_proposal.ccam) > 5:
+        print(f"    ... et {len(coding_proposal.ccam) - 5} autres actes")
+
+except Exception as e:
+    print(f"❌ Erreur lors du codage: {e}")
+    import traceback
+    traceback.print_exc()
+    exit(1)
+
+print("\n" + "=" * 60)
+print("✅ TEST TERMINÉ AVEC SUCCÈS")
+print("=" * 60)