4a12cd2676
Pipeline complet de traitement de documents médicaux PDF : - Extraction texte (pdfplumber) et classification (Trackare/CRH) - Anonymisation multi-couche (regex + NER CamemBERT + sweep) - Extraction médicale CIM-10 hybride : edsnlp (AP-HP) enrichit les diagnostics, médicaments (codes ATC via Romedi) et négation, avec fallback regex pour les patterns spécifiques - Fix sentencepiece pinné à <0.2.0 pour compatibilité CamemBERT Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
141 lines
3.4 KiB
Python
141 lines
3.4 KiB
Python
"""Pipeline edsnlp pour l'extraction médicale (CIM-10, médicaments, négation)."""
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from __future__ import annotations
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import logging
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from dataclasses import dataclass, field
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from typing import Optional
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logger = logging.getLogger(__name__)
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_nlp = None
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_available = None
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@dataclass
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class CIM10Entity:
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texte: str
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code: str
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negation: bool = False
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hypothese: bool = False
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@dataclass
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class DrugEntity:
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texte: str
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code_atc: Optional[str] = None
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negation: bool = False
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@dataclass
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class DateEntity:
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texte: str
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value: Optional[str] = None
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@dataclass
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class EdsnlpResult:
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cim10_entities: list[CIM10Entity] = field(default_factory=list)
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drug_entities: list[DrugEntity] = field(default_factory=list)
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date_entities: list[DateEntity] = field(default_factory=list)
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def is_available() -> bool:
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"""Vérifie si edsnlp est installé et utilisable."""
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global _available
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if _available is not None:
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return _available
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try:
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import edsnlp # noqa: F401
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_available = True
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except ImportError:
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_available = False
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return _available
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def get_pipeline():
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"""Retourne le pipeline edsnlp (singleton lazy-loaded)."""
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global _nlp
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if _nlp is not None:
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return _nlp
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if not is_available():
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raise RuntimeError("edsnlp n'est pas installé")
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import edsnlp
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logger.info("Initialisation du pipeline edsnlp...")
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nlp = edsnlp.blank("eds")
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nlp.add_pipe("eds.normalizer")
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nlp.add_pipe("eds.sentences")
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nlp.add_pipe("eds.cim10", config=dict(attr="NORM", term_matcher="simstring"))
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nlp.add_pipe("eds.drugs", config=dict(attr="NORM", term_matcher="exact"))
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nlp.add_pipe("eds.negation")
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nlp.add_pipe("eds.hypothesis")
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nlp.add_pipe("eds.dates")
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_nlp = nlp
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logger.info("Pipeline edsnlp initialisé avec succès")
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return _nlp
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def analyze(text: str) -> EdsnlpResult:
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"""Analyse un texte médical avec edsnlp.
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Retourne les entités CIM-10, médicaments et dates détectées.
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"""
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result = EdsnlpResult()
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if not is_available():
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return result
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try:
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nlp = get_pipeline()
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doc = nlp(text)
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except Exception:
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logger.exception("Erreur lors de l'analyse edsnlp")
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return result
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for ent in doc.ents:
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negation = getattr(ent._, "negation", False) or False
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hypothese = getattr(ent._, "hypothesis", False) or False
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if ent.label_ == "cim10":
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code = ent.kb_id_ or ""
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if code:
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result.cim10_entities.append(CIM10Entity(
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texte=ent.text,
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code=code,
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negation=negation,
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hypothese=hypothese,
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))
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elif ent.label_ == "drug":
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code_atc = ent.kb_id_ or None
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result.drug_entities.append(DrugEntity(
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texte=ent.text,
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code_atc=code_atc,
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negation=negation,
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))
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# Dates
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for span in doc.spans.get("dates", []):
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date_value = None
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if hasattr(span._, "date"):
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date_obj = span._.date
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if date_obj is not None:
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date_value = str(date_obj)
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result.date_entities.append(DateEntity(
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texte=span.text,
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value=date_value,
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))
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return result
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def reset():
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"""Réinitialise le pipeline (utile pour les tests)."""
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global _nlp, _available
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_nlp = None
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_available = None
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