feat: dictionnaire CIM-10 complet (10 893 codes) + robustesse regex
- Nouveau module cim10_dict.py : extraction depuis metadata.json FAISS, lookup intelligent avec normalisation Unicode (accents, trémas, apostrophes) - cim10_extractor : _lookup_cim10 utilise le dictionnaire complet, _find_dp normalisé, _find_das élargi à 20 patterns (cardio, métabo, infectieux, rénal...), biologie +6 tests (TGO/TGP, Hb, créatinine), traitements sans limite de lignes - document_classifier : scoring pondéré, classify_with_confidence(), scan 5000 chars - CLI --build-dict pour regénérer data/cim10_dict.json - 32 nouveaux tests unitaires (124 total, 0 échec) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
dom
@@ -38,6 +38,7 @@ OLLAMA_TIMEOUT = 120
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# --- Configuration RAG ---
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RAG_INDEX_DIR = BASE_DIR / "data" / "rag_index"
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CIM10_DICT_PATH = BASE_DIR / "data" / "cim10_dict.json"
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CIM10_PDF = Path("/home/dom/ai/aivanov_CIM/cim-10-fr_2026_a_usage_pmsi_version_provisoire_111225.pdf")
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GUIDE_METHODO_PDF = Path("/home/dom/ai/aivanov_CIM/guide_methodo_mco_2026_version_provisoire.pdf")
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CCAM_PDF = Path("/home/dom/ai/aivanov_CIM/actualisation_ccam_descriptive_a_usage_pmsi_v4_2025.pdf")
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@@ -71,6 +72,7 @@ class Diagnostic(BaseModel):
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cim10_suggestion: Optional[str] = None
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cim10_confidence: Optional[str] = None
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justification: Optional[str] = None
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raisonnement: Optional[str] = None
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sources_rag: list[RAGSource] = Field(default_factory=list)
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@@ -2,44 +2,93 @@
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from __future__ import annotations
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from dataclasses import dataclass
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@dataclass
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class ClassificationResult:
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"""Résultat de classification avec score de confiance."""
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doc_type: str
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confidence: float
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scores: dict[str, float]
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# Marqueurs pondérés : (texte, poids)
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_TRACKARE_MARKERS: list[tuple[str, int]] = [
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("ipp:", 3),
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("episode no:", 3),
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("dossier patient", 2),
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("détails des patients", 2),
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("détails épisode", 2),
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("liste des contacts", 1),
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("notes paramédicales", 1),
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("signes vitaux", 1),
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("traitements médicamenteux", 1),
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("observations médicales", 1),
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("constantes", 1),
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("prescriptions", 1),
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("presc. de sortie", 2),
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("type de note", 1),
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]
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_CRH_MARKERS: list[tuple[str, int]] = [
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("mon cher confrère", 3),
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("cher confrère", 3),
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("chère consœur", 3),
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("compte rendu d'hospitalisation", 3),
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("compte-rendu", 2),
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("service de gastro", 2),
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("service de chirurgie", 2),
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("service de médecine", 2),
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("pôle spécialités", 1),
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("votre patient", 2),
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("votre patiente", 2),
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("au total", 1),
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("ttt de sortie", 1),
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("devenir", 1),
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("cordialement", 1),
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]
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_SCAN_LENGTH = 5000
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def classify_with_confidence(text: str) -> ClassificationResult:
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"""Classifie un document avec un score de confiance.
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Retourne un ClassificationResult avec le type, la confiance (0.0-1.0),
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et les scores détaillés.
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"""
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text_lower = text[:_SCAN_LENGTH].lower()
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trackare_score = sum(weight for marker, weight in _TRACKARE_MARKERS if marker in text_lower)
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crh_score = sum(weight for marker, weight in _CRH_MARKERS if marker in text_lower)
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total = trackare_score + crh_score
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if total == 0:
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return ClassificationResult(doc_type="crh", confidence=0.5, scores={"trackare": 0, "crh": 0})
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if trackare_score > crh_score:
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confidence = trackare_score / total
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doc_type = "trackare"
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elif crh_score > trackare_score:
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confidence = crh_score / total
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doc_type = "crh"
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else:
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# Égalité — défaut CRH
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confidence = 0.5
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doc_type = "crh"
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return ClassificationResult(
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doc_type=doc_type,
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confidence=round(confidence, 2),
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scores={"trackare": trackare_score, "crh": crh_score},
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)
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def classify(text: str) -> str:
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"""Classifie un document extrait en CRH ou Trackare.
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Retourne "crh" ou "trackare".
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Signature inchangée pour rétrocompatibilité.
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"""
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text_lower = text[:3000].lower()
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trackare_markers = [
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"dossier patient",
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"détails des patients",
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"détails épisode",
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"liste des contacts",
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"notes paramédicales",
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"signes vitaux",
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"traitements médicamenteux",
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"observations médicales",
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]
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trackare_score = sum(1 for m in trackare_markers if m in text_lower)
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crh_markers = [
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"mon cher confrère",
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"cher confrère",
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"compte rendu d'hospitalisation",
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"compte-rendu",
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"service de gastro",
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"pôle spécialités",
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"votre patient",
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]
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crh_score = sum(1 for m in crh_markers if m in text_lower)
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if trackare_score >= 2:
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return "trackare"
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if crh_score >= 2:
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return "crh"
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# Heuristique : Trackare contient des tableaux avec IPP
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if "ipp:" in text_lower or "episode no:" in text_lower:
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return "trackare"
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return "crh"
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return classify_with_confidence(text).doc_type
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10
src/main.py
10
src/main.py
@@ -163,8 +163,18 @@ def main(input_path: str | None = None) -> None:
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action="store_true",
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help="Désactiver l'enrichissement RAG (FAISS + Ollama)",
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)
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parser.add_argument(
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"--build-dict",
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action="store_true",
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help="Générer le dictionnaire CIM-10 depuis metadata.json et quitter",
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)
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args = parser.parse_args()
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if args.build_dict:
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from .medical.cim10_dict import build_dict
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build_dict()
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return
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if args.no_ner:
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# Monkey-patch pour désactiver NER
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from .anonymization import ner_anonymizer
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180
src/medical/cim10_dict.py
Normal file
180
src/medical/cim10_dict.py
Normal file
@@ -0,0 +1,180 @@
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"""Dictionnaire CIM-10 complet extrait depuis les métadonnées FAISS.
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Fournit un lookup intelligent avec normalisation Unicode pour la recherche
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de codes CIM-10 à partir de textes médicaux en français.
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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import unicodedata
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from pathlib import Path
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from typing import Optional
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from ..config import CIM10_DICT_PATH, RAG_INDEX_DIR
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logger = logging.getLogger(__name__)
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# Singleton : dictionnaire chargé une seule fois
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_dict_cache: dict[str, str] | None = None
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# Cache des labels normalisés pour le substring matching
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_normalized_cache: list[tuple[str, str, str]] | None = None
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def normalize_text(text: str) -> str:
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"""Normalise un texte : accent folding, lowercase, collapse whitespace.
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Utilise unicodedata pour supprimer les accents (NFD → suppression des
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combining marks), puis met en minuscules et collapse les espaces multiples.
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"""
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# Normaliser les apostrophes Unicode → ASCII
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text = text.replace("\u2019", "'").replace("\u2018", "'").replace("\u02BC", "'")
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# NFD decomposition puis suppression des combining marks (accents)
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nfkd = unicodedata.normalize("NFKD", text)
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stripped = "".join(c for c in nfkd if unicodedata.category(c) != "Mn")
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# Lowercase + collapse whitespace
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return re.sub(r"\s+", " ", stripped.lower()).strip()
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def build_dict() -> dict[str, str]:
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"""Construit le dictionnaire CIM-10 depuis metadata.json et l'écrit dans data/cim10_dict.json.
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Extrait le code et le label (première ligne de l'extrait, sans le préfixe code)
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depuis chaque entrée CIM-10 du metadata.json existant.
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Returns:
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Le dictionnaire code → label.
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"""
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metadata_path = RAG_INDEX_DIR / "metadata.json"
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if not metadata_path.exists():
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logger.error("metadata.json non trouvé : %s", metadata_path)
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return {}
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with open(metadata_path, encoding="utf-8") as f:
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metadata = json.load(f)
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result: dict[str, str] = {}
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for entry in metadata:
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if entry.get("document") != "cim10":
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continue
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code = entry.get("code")
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extrait = entry.get("extrait", "")
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if not code or not extrait:
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continue
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# Extraire le label : première ligne, sans le préfixe "CODE "
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first_line = extrait.split("\n")[0].strip()
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# Retirer le préfixe code (ex: "K85.1 Pancréatite aigüe...")
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prefix = f"{code} "
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if first_line.startswith(prefix):
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label = first_line[len(prefix):]
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else:
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label = first_line
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# Garder l'entrée la plus spécifique (avec point > sans point)
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if code not in result or not label:
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result[code] = label
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# Écrire le fichier JSON
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CIM10_DICT_PATH.parent.mkdir(parents=True, exist_ok=True)
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with open(CIM10_DICT_PATH, "w", encoding="utf-8") as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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logger.info("Dictionnaire CIM-10 généré : %d codes → %s", len(result), CIM10_DICT_PATH)
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return result
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def load_dict() -> dict[str, str]:
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"""Charge le dictionnaire CIM-10 (singleton lazy-loaded).
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Si le fichier JSON n'existe pas, tente de le construire depuis metadata.json.
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"""
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global _dict_cache
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if _dict_cache is not None:
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return _dict_cache
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if CIM10_DICT_PATH.exists():
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with open(CIM10_DICT_PATH, encoding="utf-8") as f:
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_dict_cache = json.load(f)
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else:
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logger.info("Dictionnaire CIM-10 absent, construction depuis metadata.json...")
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_dict_cache = build_dict()
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return _dict_cache
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def _get_normalized_entries() -> list[tuple[str, str, str]]:
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"""Retourne une liste de (code, label_original, label_normalisé) triée par spécificité.
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Les codes avec point (sous-codes, plus spécifiques) sont en premier.
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"""
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global _normalized_cache
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if _normalized_cache is not None:
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return _normalized_cache
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d = load_dict()
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entries = []
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for code, label in d.items():
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norm = normalize_text(label)
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entries.append((code, label, norm))
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# Trier : sous-codes (avec point) d'abord, puis par longueur de label décroissante
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# pour préférer les matchs les plus spécifiques
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entries.sort(key=lambda e: (0 if "." in e[0] else 1, -len(e[2])))
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_normalized_cache = entries
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return _normalized_cache
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def lookup(
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text: str,
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domain_overrides: dict[str, str] | None = None,
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) -> str | None:
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"""Recherche un code CIM-10 pour un texte donné.
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Stratégie en 3 niveaux :
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1. Match substring dans domain_overrides (prioritaire, ex: CIM10_MAP existant)
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2. Match exact normalisé dans le dictionnaire complet
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3. Match substring normalisé avec scoring par spécificité (préfère sous-codes)
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Args:
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text: Le texte médical à rechercher.
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domain_overrides: Dictionnaire terme→code prioritaire (ex: CIM10_MAP).
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Returns:
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Le code CIM-10 trouvé ou None.
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"""
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if not text:
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return None
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text_norm = normalize_text(text)
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# Niveau 1 : domain overrides (substring match)
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if domain_overrides:
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for terme, code in domain_overrides.items():
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if normalize_text(terme) in text_norm:
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return code
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# Niveau 2 : match exact normalisé dans le dictionnaire complet
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d = load_dict()
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for code, label in d.items():
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if normalize_text(label) == text_norm:
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return code
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# Niveau 3 : substring match normalisé (plus spécifique d'abord)
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entries = _get_normalized_entries()
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for code, _label, norm_label in entries:
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if not norm_label or len(norm_label) < 4:
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continue
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if norm_label in text_norm:
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return code
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return None
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def reset_cache() -> None:
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"""Réinitialise les caches (utile pour les tests)."""
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global _dict_cache, _normalized_cache
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_dict_cache = None
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_normalized_cache = None
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@@ -9,6 +9,7 @@ from typing import Optional
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logger = logging.getLogger(__name__)
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from .cim10_dict import lookup as dict_lookup, normalize_text
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from ..config import (
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ActeCCAM,
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BiologieCle,
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@@ -253,33 +254,77 @@ def _extract_diagnostics(
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def _find_diagnostic_principal(text_lower: str, conclusion: str) -> Diagnostic | None:
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"""Trouve le diagnostic principal dans le texte."""
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conclusion_lower = conclusion.lower()
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"""Trouve le diagnostic principal dans le texte.
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# Chercher dans la conclusion d'abord
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Normalise le texte avant matching pour gérer les variations d'accents/casse.
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"""
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conclusion_norm = normalize_text(conclusion)
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# Chercher dans la conclusion d'abord via CIM10_MAP (domain override)
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for terme, code in CIM10_MAP.items():
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if terme in conclusion_lower:
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if normalize_text(terme) in conclusion_norm:
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return Diagnostic(texte=terme.capitalize(), cim10_suggestion=code)
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# Patterns courants pour le DP
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text_norm = normalize_text(text_lower)
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# Patterns courants pour le DP (normalisés, sans accents)
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dp_patterns = [
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r"pancréatite\s+aigu[eë]\s+(?:d'origine\s+)?lithiasique",
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r"pancréatite\s+aigu[eë]\s+biliaire",
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r"pancréatite\s+aigu[eë]",
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r"pancreatite\s+aigue\s+(?:d'origine\s+)?lithiasique",
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r"pancreatite\s+aigue\s+biliaire",
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r"pancreatite\s+aigue",
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]
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for pat in dp_patterns:
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if re.search(pat, text_lower):
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matched = re.search(pat, text_lower).group(0)
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m = re.search(pat, text_norm)
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if m:
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matched = m.group(0)
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code = _lookup_cim10(matched)
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return Diagnostic(texte=matched.capitalize(), cim10_suggestion=code)
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return None
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# Patterns DAS : (pattern_normalisé, label, code_fallback)
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# Les patterns sont appliqués sur du texte normalisé (sans accents, lowercase)
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_DAS_PATTERNS: list[tuple[str, str, str]] = [
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# Lithiases biliaires
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(r"lithiase\s+(?:du\s+)?(?:bas\s+)?choledoque", "Lithiase du cholédoque", "K80.5"),
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(r"vesicule\s+lithiasique|lithiases?\s+vesiculaire", "Lithiase vésiculaire", "K80.2"),
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# Inflammation biliaire
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(r"cholecystite\s+aigue", "Cholécystite aiguë", "K81.0"),
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(r"angiocholite|cholangite", "Angiocholite", "K83.0"),
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# Réactions médicamenteuses
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(r"eruption\s+cutanee|toxidermie|reaction\s+au\s+tramadol", "Éruption cutanée médicamenteuse", "L27.0"),
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# Cardiovasculaire
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(r"hypertension\s+arterielle|\bhta\b", "Hypertension artérielle", "I10"),
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(r"fibrillation\s+auriculaire|\bfa\b(?:\s+paroxystique)?|\bacfa\b", "Fibrillation auriculaire", "I48.9"),
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(r"embolie\s+pulmonaire", "Embolie pulmonaire", "I26.9"),
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(r"thrombose\s+veineuse\s+profonde|\btvp\b", "Thrombose veineuse profonde", "I80.2"),
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# Métabolique
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(r"diabete\s+(?:sucre\s+)?(?:de\s+)?type\s+2|diabete\s+type\s*2", "Diabète de type 2", "E11.9"),
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(r"diabete\s+(?:sucre\s+)?(?:de\s+)?type\s+1|diabete\s+type\s*1", "Diabète de type 1", "E10.9"),
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(r"dyslipidemie|hypercholesterolemie", "Dyslipidémie", "E78.5"),
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(r"denutrition|malnutrition", "Dénutrition", "E46"),
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# Infectieux
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(r"pneumopathie|pneumonie", "Pneumopathie", "J18.9"),
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(r"infection\s+urinaire|pyelonephrite", "Infection urinaire", "N39.0"),
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(r"\bsepsis\b|septicemie|choc\s+septique", "Sepsis", "A41.9"),
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# Rénal
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(r"insuffisance\s+renale", "Insuffisance rénale", "N19"),
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||||
# Hématologique
|
||||
(r"anemie", "Anémie", "D64.9"),
|
||||
# Addictions
|
||||
(r"tabagisme|tabac\s+actif", "Tabagisme", "F17.2"),
|
||||
(r"ethylisme|alcoolisme|intoxication\s+ethylique", "Éthylisme", "F10.1"),
|
||||
]
|
||||
|
||||
|
||||
def _find_diagnostics_associes(
|
||||
text_lower: str, conclusion: str, dossier: DossierMedical
|
||||
) -> list[Diagnostic]:
|
||||
"""Trouve les diagnostics associés."""
|
||||
"""Trouve les diagnostics associés.
|
||||
|
||||
Utilise des patterns normalisés (sans accents) pour une détection robuste.
|
||||
"""
|
||||
das: list[Diagnostic] = []
|
||||
existing_codes = set()
|
||||
if dossier.diagnostic_principal:
|
||||
@@ -287,33 +332,22 @@ def _find_diagnostics_associes(
|
||||
for d in dossier.diagnostics_associes:
|
||||
existing_codes.add(d.cim10_suggestion)
|
||||
|
||||
# Lithiase cholédoque
|
||||
if re.search(r"lithiase\s+(?:du\s+)?(?:bas\s+)?cholédoque", text_lower):
|
||||
if "K80.5" not in existing_codes:
|
||||
das.append(Diagnostic(texte="Lithiase du cholédoque", cim10_suggestion="K80.5"))
|
||||
existing_codes.add("K80.5")
|
||||
text_norm = normalize_text(text_lower)
|
||||
|
||||
# Éruption médicamenteuse
|
||||
if re.search(r"éruption\s+cutanée|eruption\s+cutanée|toxidermie|réaction\s+au\s+tramadol", text_lower):
|
||||
if "L27.0" not in existing_codes:
|
||||
das.append(Diagnostic(texte="Éruption cutanée médicamenteuse", cim10_suggestion="L27.0"))
|
||||
existing_codes.add("L27.0")
|
||||
# Patterns DAS
|
||||
for pat, label, code in _DAS_PATTERNS:
|
||||
if re.search(pat, text_norm) and code not in existing_codes:
|
||||
das.append(Diagnostic(texte=label, cim10_suggestion=code))
|
||||
existing_codes.add(code)
|
||||
|
||||
# Obésité (IMC >= 30)
|
||||
if re.search(r"imc\s*[:=]?\s*(\d{2,3}[.,]\d+)", text_lower):
|
||||
m = re.search(r"imc\s*[:=]?\s*(\d{2,3}[.,]\d+)", text_lower)
|
||||
# Obésité (IMC >= 30) — pattern spécial avec extraction de valeur
|
||||
m = re.search(r"imc\s*[:=]?\s*(\d{2,3}[.,]\d+)", text_norm)
|
||||
if m:
|
||||
imc_val = float(m.group(1).replace(",", "."))
|
||||
if imc_val >= 30 and "E66.0" not in existing_codes:
|
||||
das.append(Diagnostic(texte=f"Obésité (IMC {imc_val})", cim10_suggestion="E66.0"))
|
||||
existing_codes.add("E66.0")
|
||||
|
||||
# Lithiases vésiculaires
|
||||
if re.search(r"vésicule\s+lithiasique|lithiases?\s+vésiculaire", text_lower):
|
||||
if "K80.2" not in existing_codes:
|
||||
das.append(Diagnostic(texte="Lithiase vésiculaire", cim10_suggestion="K80.2"))
|
||||
existing_codes.add("K80.2")
|
||||
|
||||
return das
|
||||
|
||||
|
||||
@@ -399,7 +433,7 @@ def _extract_traitements(
|
||||
if not drug.negation and drug.code_atc:
|
||||
drug_atc[drug.texte.lower()] = drug.code_atc
|
||||
|
||||
# Depuis le texte — section "TTT de sortie" (limiter à quelques lignes)
|
||||
# Depuis le texte — section "TTT de sortie" (sans limite de lignes)
|
||||
m = re.search(
|
||||
r"(?:TTT|Traitement)\s+de\s+sortie\s*[:：]?\s*\n?(.*?)(?=\n\s*(?:Devenir|Rédigé|Cordialement|Patient:|Episode|Le \d{2}/\d{2}|\n\n)|$)",
|
||||
text,
|
||||
@@ -408,17 +442,28 @@ def _extract_traitements(
|
||||
if m:
|
||||
block = m.group(1).strip()
|
||||
lines = block.split("\n")
|
||||
for line in lines[:10]: # Limiter à 10 lignes max
|
||||
for line in lines:
|
||||
line = line.strip().lstrip("- •")
|
||||
if not line or len(line) <= 2:
|
||||
continue
|
||||
# Ignorer les footers et lignes non-médicament
|
||||
if re.match(r"^(Patient|Episode|Le \d|Page|V\d)", line):
|
||||
# Conditions d'arrêt : footers, signatures, metadata
|
||||
if re.match(
|
||||
r"^(Patient|Episode|Le \d|Page\s+\d|V\d|Rédigé|Cordialement|Dr\s|Docteur|Signature|Date|Fait\s+le)",
|
||||
line,
|
||||
re.IGNORECASE,
|
||||
):
|
||||
break
|
||||
med = line
|
||||
poso = None
|
||||
# Séparer médicament et posologie
|
||||
poso_match = re.search(r"\s+(si besoin|matin|soir|midi|\d+\s*(?:mg|cp|gel).*)", line, re.IGNORECASE)
|
||||
# Séparer médicament et posologie (pattern élargi)
|
||||
poso_match = re.search(
|
||||
r"\s+(si besoin|matin|soir|midi|"
|
||||
r"\d+\s*(?:mg|cp|gel|sachet|comprim[ée]|g[ée]lule).*|"
|
||||
r"\d+\s*(?:x|fois)\s*/?\s*(?:j(?:our)?|semaine)|"
|
||||
r"pendant\s+\d+\s*jours?)",
|
||||
line,
|
||||
re.IGNORECASE,
|
||||
)
|
||||
if poso_match:
|
||||
med = line[:poso_match.start()].strip()
|
||||
poso = poso_match.group(1).strip()
|
||||
@@ -460,16 +505,24 @@ def _match_drug_atc(med_name: str, drug_atc: dict[str, str]) -> Optional[str]:
|
||||
|
||||
|
||||
def _extract_biologie(text: str, dossier: DossierMedical) -> None:
|
||||
"""Extrait les résultats biologiques clés."""
|
||||
"""Extrait les résultats biologiques clés.
|
||||
|
||||
Supporte les aliases (TGO/TGP, Hb), variantes d'unités (UI/L, µmol/L, g/dL),
|
||||
et des tests additionnels (hémoglobine, plaquettes, leucocytes, créatinine).
|
||||
"""
|
||||
bio_patterns = [
|
||||
(r"[Ll]ipas[ée]mie\s*(?:[àa=:])?\s*(\d+)", "Lipasémie", None),
|
||||
(r"CRP\s*[=:à]?\s*(\d+(?:[.,]\d+)?)", "CRP", None),
|
||||
(r"ASAT\s*[=:à]?\s*([\d.,]+)\s*(?:N|U/L)?", "ASAT", None),
|
||||
(r"ALAT\s*[=:à]?\s*([\d.,]+)\s*(?:N|U/L)?", "ALAT", None),
|
||||
(r"GGT\s*[=:à]?\s*(\d+)\s*(?:U/L)?", "GGT", None),
|
||||
(r"PAL\s*[=:à]?\s*(\d+)\s*(?:U/L)?", "PAL", None),
|
||||
(r"[Bb]ilirubine\s+(?:totale\s+)?[àa=:]\s*(\d+)\s*(?:µmol/L)?", "Bilirubine totale", None),
|
||||
(r"troponine\s+(négative|positive|normale)", "Troponine", None),
|
||||
(r"[Ll]ipas[ée]mie\s*(?:[àa=:])?\s*(\d+)\s*(?:UI/L|U/L)?", "Lipasémie", None),
|
||||
(r"CRP\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:mg/[Ll])?", "CRP", None),
|
||||
(r"(?:ASAT|TGO)\s*[=:àa]?\s*([\d.,]+)\s*(?:N|U(?:I)?/L)?", "ASAT", None),
|
||||
(r"(?:ALAT|TGP)\s*[=:àa]?\s*([\d.,]+)\s*(?:N|U(?:I)?/L)?", "ALAT", None),
|
||||
(r"GGT\s*[=:àa]?\s*(\d+)\s*(?:U(?:I)?/L)?", "GGT", None),
|
||||
(r"PAL\s*[=:àa]?\s*(\d+)\s*(?:U(?:I)?/L)?", "PAL", None),
|
||||
(r"[Bb]ilirubine\s+(?:totale\s+)?[àa=:]\s*(\d+(?:[.,]\d+)?)\s*(?:µmol/L|mg/dL)?", "Bilirubine totale", None),
|
||||
(r"[Tt]roponine\s+(?:us\s+)?(n[ée]gative|positive|normale)", "Troponine", None),
|
||||
(r"(?:[Hh][ée]moglobine|Hb)\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:g/dL|g/L)?", "Hémoglobine", None),
|
||||
(r"[Pp]laquettes?\s*[=:àa]?\s*(\d+(?:\s*000)?)\s*(?:/mm3|G/L)?", "Plaquettes", None),
|
||||
(r"[Ll]eucocytes?\s*[=:àa]?\s*(\d+(?:\s*000)?)\s*(?:/mm3|G/L)?", "Leucocytes", None),
|
||||
(r"[Cc]r[ée]atinine?\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:µmol/L|mg/dL)?", "Créatinine", None),
|
||||
]
|
||||
|
||||
for pattern, test_name, _ in bio_patterns:
|
||||
@@ -589,12 +642,11 @@ def _find_act_date(text: str, act_pattern: str) -> str | None:
|
||||
|
||||
|
||||
def _lookup_cim10(text: str) -> str | None:
|
||||
"""Cherche un code CIM-10 pour un texte donné."""
|
||||
text_lower = text.lower().strip()
|
||||
for terme, code in CIM10_MAP.items():
|
||||
if terme in text_lower:
|
||||
return code
|
||||
return None
|
||||
"""Cherche un code CIM-10 pour un texte donné.
|
||||
|
||||
Utilise le dictionnaire complet (10 893 codes) avec CIM10_MAP en override prioritaire.
|
||||
"""
|
||||
return dict_lookup(text, domain_overrides=CIM10_MAP)
|
||||
|
||||
|
||||
def _is_abnormal(test: str, value: str) -> bool | None:
|
||||
@@ -616,6 +668,10 @@ def _is_abnormal(test: str, value: str) -> bool | None:
|
||||
"GGT": (0, 60),
|
||||
"PAL": (0, 150),
|
||||
"Bilirubine totale": (0, 17),
|
||||
"Hémoglobine": (12, 17),
|
||||
"Plaquettes": (150, 400),
|
||||
"Leucocytes": (4, 10),
|
||||
"Créatinine": (50, 120),
|
||||
}
|
||||
|
||||
if test in normals:
|
||||
|
||||
@@ -8,6 +8,8 @@ from src.medical.cim10_extractor import (
|
||||
_lookup_cim10,
|
||||
_is_abnormal,
|
||||
)
|
||||
from src.medical.cim10_dict import normalize_text, load_dict, lookup, reset_cache
|
||||
from src.extraction.document_classifier import classify, classify_with_confidence
|
||||
|
||||
|
||||
class TestCIM10Lookup:
|
||||
@@ -236,3 +238,221 @@ Devenir : sortie le 03/03."""
|
||||
complication_terms = [c.lower() for c in dossier.complications]
|
||||
assert "fièvre" not in complication_terms
|
||||
assert "infection" not in complication_terms
|
||||
|
||||
|
||||
# === Nouveaux tests : dictionnaire CIM-10, normalisation, robustesse ===
|
||||
|
||||
|
||||
class TestCIM10Dict:
|
||||
"""Tests pour le chargement du dictionnaire CIM-10 complet."""
|
||||
|
||||
def test_load_dict_not_empty(self):
|
||||
d = load_dict()
|
||||
assert len(d) > 10000
|
||||
|
||||
def test_known_codes_present(self):
|
||||
d = load_dict()
|
||||
assert "K85.1" in d
|
||||
assert "K80.5" in d
|
||||
assert "I10" in d
|
||||
assert "E66.0" in d
|
||||
assert "L27.0" in d
|
||||
|
||||
def test_labels_non_empty(self):
|
||||
d = load_dict()
|
||||
for code, label in list(d.items())[:100]:
|
||||
assert label, f"Label vide pour {code}"
|
||||
|
||||
|
||||
class TestNormalizeText:
|
||||
"""Tests pour normalize_text : accents, casse, whitespace."""
|
||||
|
||||
def test_accents_removed(self):
|
||||
assert normalize_text("Pancréatite") == "pancreatite"
|
||||
|
||||
def test_lowercase(self):
|
||||
assert normalize_text("PANCRÉATITE AIGUË") == "pancreatite aigue"
|
||||
|
||||
def test_whitespace_collapsed(self):
|
||||
assert normalize_text(" pancréatite aiguë ") == "pancreatite aigue"
|
||||
|
||||
def test_trema(self):
|
||||
assert normalize_text("aigüe") == "aigue"
|
||||
|
||||
def test_mixed(self):
|
||||
assert normalize_text("Éruption Cutanée Médicamenteuse") == "eruption cutanee medicamenteuse"
|
||||
|
||||
|
||||
class TestDictLookup:
|
||||
"""Tests pour lookup : priorité domain override, match exact, substring."""
|
||||
|
||||
def test_domain_override_priority(self):
|
||||
"""CIM10_MAP (override) a priorité sur le dictionnaire complet."""
|
||||
override = {"pancréatite aiguë biliaire": "K85.1"}
|
||||
result = lookup("pancréatite aiguë biliaire", domain_overrides=override)
|
||||
assert result == "K85.1"
|
||||
|
||||
def test_exact_normalized_match(self):
|
||||
"""Match exact normalisé dans le dictionnaire complet."""
|
||||
# "Hypertension essentielle (primitive)" est le label exact de I10
|
||||
result = lookup("Hypertension essentielle (primitive)")
|
||||
assert result == "I10"
|
||||
|
||||
def test_substring_match(self):
|
||||
"""Match substring normalisé."""
|
||||
result = lookup("patient avec cholécystite aiguë sévère")
|
||||
assert result == "K81.0"
|
||||
|
||||
def test_unknown_returns_none(self):
|
||||
result = lookup("texte complètement inconnu xyz123")
|
||||
assert result is None
|
||||
|
||||
def test_accent_insensitive(self):
|
||||
"""La recherche ignore les accents."""
|
||||
result = lookup("pancreatite aigue d'origine biliaire")
|
||||
assert result == "K85.1"
|
||||
|
||||
|
||||
class TestDiagnosticAccentVariations:
|
||||
"""Tests pour la détection de diagnostics avec variations d'accents."""
|
||||
|
||||
def _extract(self, text: str) -> DossierMedical:
|
||||
parsed = {
|
||||
"type": "crh",
|
||||
"patient": {"sexe": "M"},
|
||||
"sejour": {},
|
||||
"diagnostics": [],
|
||||
}
|
||||
return extract_medical_info(parsed, text)
|
||||
|
||||
def test_pancreatite_sans_accents(self):
|
||||
dossier = self._extract("Pancreatite aigue biliaire.\nDevenir : retour.")
|
||||
assert dossier.diagnostic_principal is not None
|
||||
assert dossier.diagnostic_principal.cim10_suggestion == "K85.1"
|
||||
|
||||
def test_pancreatite_trema(self):
|
||||
dossier = self._extract("Pancréatite aigüe biliaire.\nDevenir : retour.")
|
||||
assert dossier.diagnostic_principal is not None
|
||||
assert dossier.diagnostic_principal.cim10_suggestion == "K85.1"
|
||||
|
||||
def test_pancreatite_majuscules(self):
|
||||
dossier = self._extract("PANCREATITE AIGUE BILIAIRE.\nDevenir : retour.")
|
||||
assert dossier.diagnostic_principal is not None
|
||||
assert dossier.diagnostic_principal.cim10_suggestion == "K85.1"
|
||||
|
||||
def test_hta_as_das(self):
|
||||
"""HTA détectée comme DAS même sans accent."""
|
||||
dossier = self._extract("Douleur abdominale.\nhypertension arterielle connue.\nDevenir : retour.")
|
||||
codes = {d.cim10_suggestion for d in dossier.diagnostics_associes}
|
||||
assert "I10" in codes
|
||||
|
||||
|
||||
class TestBiologieEdgeCases:
|
||||
"""Tests pour l'extraction biologie avec variantes."""
|
||||
|
||||
def _extract_bio(self, text: str) -> list:
|
||||
parsed = {
|
||||
"type": "crh",
|
||||
"patient": {"sexe": "M"},
|
||||
"sejour": {},
|
||||
"diagnostics": [],
|
||||
}
|
||||
dossier = extract_medical_info(parsed, text)
|
||||
return dossier.biologie_cle
|
||||
|
||||
def test_crp_with_unit(self):
|
||||
bio = self._extract_bio("CRP=45 mg/L")
|
||||
assert any(b.test == "CRP" and b.valeur == "45" for b in bio)
|
||||
|
||||
def test_lipasemie_ui_l(self):
|
||||
bio = self._extract_bio("Lipasémie à 850 UI/L")
|
||||
assert any(b.test == "Lipasémie" and b.valeur == "850" for b in bio)
|
||||
|
||||
def test_troponine_us(self):
|
||||
bio = self._extract_bio("Troponine us négative")
|
||||
assert any(b.test == "Troponine" and b.valeur == "négative" for b in bio)
|
||||
|
||||
def test_hb_shorthand(self):
|
||||
bio = self._extract_bio("Hb = 11.5 g/dL")
|
||||
assert any(b.test == "Hémoglobine" and b.valeur == "11.5" for b in bio)
|
||||
|
||||
def test_tgo_alias(self):
|
||||
bio = self._extract_bio("TGO = 120 UI/L")
|
||||
assert any(b.test == "ASAT" and b.valeur == "120" for b in bio)
|
||||
|
||||
def test_creatinine(self):
|
||||
bio = self._extract_bio("Créatinine à 95 µmol/L")
|
||||
assert any(b.test == "Créatinine" and b.valeur == "95" for b in bio)
|
||||
|
||||
|
||||
class TestTraitementEdgeCases:
|
||||
"""Tests pour l'extraction des traitements."""
|
||||
|
||||
def _extract_ttt(self, text: str) -> list:
|
||||
parsed = {
|
||||
"type": "crh",
|
||||
"patient": {"sexe": "M"},
|
||||
"sejour": {},
|
||||
"diagnostics": [],
|
||||
}
|
||||
dossier = extract_medical_info(parsed, text)
|
||||
return dossier.traitements_sortie
|
||||
|
||||
def test_more_than_10_medications(self):
|
||||
"""Vérifie que la limite de 10 est supprimée."""
|
||||
meds = "\n".join(f"Médicament{i} 100mg matin" for i in range(15))
|
||||
text = f"TTT de sortie :\n{meds}\n\nDevenir : retour."
|
||||
ttt = self._extract_ttt(text)
|
||||
assert len(ttt) >= 15
|
||||
|
||||
def test_posologie_sachet(self):
|
||||
text = "TTT de sortie :\nMovicol 1 sachet matin\n\nDevenir : retour."
|
||||
ttt = self._extract_ttt(text)
|
||||
assert len(ttt) >= 1
|
||||
|
||||
def test_posologie_x_par_jour(self):
|
||||
text = "TTT de sortie :\nParacétamol 1g 3x/jour\n\nDevenir : retour."
|
||||
ttt = self._extract_ttt(text)
|
||||
assert len(ttt) >= 1
|
||||
assert ttt[0].posologie is not None
|
||||
|
||||
def test_stop_on_footer(self):
|
||||
text = "TTT de sortie :\nParacétamol\nDoliprane\nDr Martin signature\nAutre médicament\n\nDevenir : retour."
|
||||
ttt = self._extract_ttt(text)
|
||||
meds = [t.medicament for t in ttt]
|
||||
assert "Autre médicament" not in meds
|
||||
|
||||
def test_pendant_x_jours(self):
|
||||
text = "TTT de sortie :\nAmoxicilline 1g pendant 7 jours\n\nDevenir : retour."
|
||||
ttt = self._extract_ttt(text)
|
||||
assert len(ttt) >= 1
|
||||
assert ttt[0].posologie is not None
|
||||
assert "7 jours" in ttt[0].posologie
|
||||
|
||||
|
||||
class TestClassifierConfidence:
|
||||
"""Tests pour classify_with_confidence."""
|
||||
|
||||
def test_high_confidence_trackare(self):
|
||||
text = "Dossier Patient\nIPP: 12345\nDétails épisode\nEpisode No: 67890\nSignes vitaux\n"
|
||||
result = classify_with_confidence(text)
|
||||
assert result.doc_type == "trackare"
|
||||
assert result.confidence >= 0.7
|
||||
|
||||
def test_high_confidence_crh(self):
|
||||
text = "Mon cher confrère,\nCompte rendu d'hospitalisation\nVotre patient a été admis dans le service de gastro\n"
|
||||
result = classify_with_confidence(text)
|
||||
assert result.doc_type == "crh"
|
||||
assert result.confidence >= 0.7
|
||||
|
||||
def test_ambiguous_case(self):
|
||||
text = "Document médical quelconque sans marqueurs spécifiques."
|
||||
result = classify_with_confidence(text)
|
||||
assert result.confidence <= 0.6
|
||||
|
||||
def test_backward_compatible(self):
|
||||
"""classify() retourne toujours une string."""
|
||||
text = "Dossier Patient\nIPP: 12345\n"
|
||||
result = classify(text)
|
||||
assert isinstance(result, str)
|
||||
assert result in ("crh", "trackare")
|
||||
|
||||
Reference in New Issue
Block a user