feat(ner-first): add NER-first architecture scaffolding (steps 1-4)
Add infrastructure for NER-first name validation without changing existing behavior. New code only, quality score remains 100/100. Step 1: Load INSEE family names (219K) and prenoms (33K) as module-level gazetteers (_INSEE_NOMS_FAMILLE, _INSEE_PRENOMS_SET) normalized uppercase without accents. Step 2: Add _run_ner_on_original_text() that runs all available NER models (EDS-Pseudo, GLiNER, CamemBERT-bio) on unmasked text and returns deduplicated NerDetection list. Step 3: Add NerDetection and NameCandidate dataclasses. Modify _extract_document_names and _extract_trackare_identity to also return NameCandidate lists with context_strength (high/medium/low) metadata. Callers updated for new return values. Step 4: Add _cross_validate_name_candidates() implementing decision matrix: high context always accepted, medium/low validated against NER confirmations, INSEE membership, and stopword filtering. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -151,6 +151,50 @@ def _load_insee_gazetteers():
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_load_insee_gazetteers()
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# ----------------- Gazetteers INSEE noms de famille + prénoms (uppercase, sans accents) ---------
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_INSEE_NOMS_FAMILLE: set = set()
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_INSEE_PRENOMS_SET: set = set()
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def _normalize_nfkd_upper(s: str) -> str:
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"""Supprime les accents et met en majuscules (pour matching INSEE)."""
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import unicodedata
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return "".join(
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c for c in unicodedata.normalize("NFD", s)
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if unicodedata.category(c) != "Mn"
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).upper()
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def _load_insee_noms_prenoms():
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"""Charge noms de famille et prénoms INSEE, normalisés uppercase sans accents."""
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global _INSEE_NOMS_FAMILLE, _INSEE_PRENOMS_SET
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data_dir = Path(__file__).parent / "data" / "insee"
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noms_path = data_dir / "noms_famille_france.txt"
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if noms_path.exists():
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try:
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_INSEE_NOMS_FAMILLE = {
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_normalize_nfkd_upper(line.strip())
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for line in noms_path.read_text(encoding="utf-8").splitlines()
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if line.strip() and len(line.strip()) >= 3
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}
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log.info(f"Gazetteers INSEE noms de famille: {len(_INSEE_NOMS_FAMILLE)} entrées")
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except Exception as e:
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log.warning(f"Erreur chargement noms de famille INSEE: {e}")
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prenoms_path = data_dir / "prenoms_france.txt"
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if prenoms_path.exists():
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try:
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_INSEE_PRENOMS_SET = {
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_normalize_nfkd_upper(line.strip())
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for line in prenoms_path.read_text(encoding="utf-8").splitlines()
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if line.strip() and len(line.strip()) >= 3
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}
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log.info(f"Gazetteers INSEE prénoms (set): {len(_INSEE_PRENOMS_SET)} entrées")
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except Exception as e:
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log.warning(f"Erreur chargement prénoms INSEE (set): {e}")
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_load_insee_noms_prenoms()
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# ----------------- Gazetteer FINESS (établissements de santé) -----------------
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_FINESS_NUMBERS: set = set() # numéros FINESS 9 chiffres
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_FINESS_ETAB_NAMES: set = set() # noms d'établissements (lowercase)
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@@ -1061,6 +1105,25 @@ class AnonResult:
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audit: List[PiiHit] = field(default_factory=list)
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is_trackare: bool = False
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@dataclass
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class NerDetection:
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"""Détection NER sur le texte original (non masqué).
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Utilisé par l'architecture NER-first pour la validation croisée des noms."""
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token: str
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label: str # NOM, PRENOM, HOPITAL, VILLE, LOC, ORG
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score: float
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page_idx: int
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source: str # "eds_pseudo", "gliner", "camembert_bio"
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@dataclass
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class NameCandidate:
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"""Candidat de nom extrait par regex avec métadonnées de confiance.
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Utilisé pour la validation croisée NER-first."""
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token: str
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source: str # regex ou champ qui a capturé ce nom
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context_strength: str # "high", "medium", "low"
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bypass_stopwords: bool # ce que force_names signifie actuellement
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# ----------------- Config loader -----------------
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def load_dictionaries(config_path: Optional[Path]) -> Dict[str, Any]:
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@@ -1708,21 +1771,36 @@ def _is_trackare_document(text: str) -> bool:
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return sum(1 for m in markers if m.lower() in t) >= 2
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def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
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def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit], set, List[NameCandidate]]:
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"""Parse les champs structurés d'un document Trackare pour extraire les PII.
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Retourne (name_tokens, pii_hits) avec les noms à masquer et les hits additionnels."""
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Retourne (name_tokens, pii_hits, force_names, candidates) avec les noms à masquer,
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les hits additionnels, le sous-ensemble force_names, et les NameCandidate
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pour la validation croisée NER-first."""
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names: set = set()
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hits: List[PiiHit] = []
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candidates: List[NameCandidate] = []
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force_names: set = set() # noms issus de contextes structurés (DR., Signé, etc.) → bypass stop words
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def _add_name(s: str):
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def _add_candidate(token: str, source: str, strength: str, bypass: bool):
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"""Ajoute un NameCandidate à la liste."""
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token = token.strip(" .-'(),")
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if len(token) < 4:
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return
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candidates.append(NameCandidate(
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token=token, source=source,
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context_strength=strength, bypass_stopwords=bypass,
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))
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def _add_name(s: str, _cand_source: str = "", _cand_strength: str = "medium"):
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s = s.strip()
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parts = s.split()
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for tok in parts:
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tok = tok.strip(" .-'(),")
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if len(tok) >= 4 and tok[0].isupper():
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names.add(tok)
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if _cand_source:
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_add_candidate(tok, _cand_source, _cand_strength, False)
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# Garder aussi le nom composé complet (DI LULLO, LE MOIGNE, etc.)
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if len(parts) >= 2:
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compound = " ".join(t.strip(" .-'(),") for t in parts if len(t.strip(" .-'(),")) >= 2)
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@@ -1738,12 +1816,14 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
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"préremplie",
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}
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def _add_name_force(tok: str):
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def _add_name_force(tok: str, _cand_source: str = "", _cand_strength: str = "medium"):
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"""Ajoute un nom depuis un contexte structuré fiable (DR., Signé direct, Note d'évolution).
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Bypass les stop words généraux mais filtre médicaments et termes de soins courants."""
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tok = tok.strip(" .-'(),")
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if len(tok) < 4 or not tok[0].isupper():
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return
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if _cand_source:
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_add_candidate(tok, _cand_source, _cand_strength, True)
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if tok.lower() in _FORCE_EXCLUDE:
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return
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# Filtre supplémentaire : ne pas force-add les mots médicaux connus
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@@ -1752,18 +1832,18 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
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names.add(tok)
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force_names.add(tok)
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# --- Identité patient ---
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# --- Identité patient (high context: DPI structured fields) ---
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# Nom de naissance: DIEGO (peut apparaître 2x : en-tête + récap tabulaire)
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for m in re.finditer(r"Nom\s+de\s+naissance\s*:\s*(.+?)(?:\s+IPP\b|\s*$)", full_text, re.MULTILINE):
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_add_name(m.group(1).strip())
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_add_name(m.group(1).strip(), "trackare_nom_naissance", "high")
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# Nom et Prénom: DIEGO PATRICIA
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for m in re.finditer(r"Nom\s+et\s+Pr[ée]nom\s*:\s*(.+?)(?:\s+Date\s+de\s+naissance|\s*$)", full_text, re.MULTILINE):
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_add_name(m.group(1).strip())
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_add_name(m.group(1).strip(), "trackare_nom_prenom", "high")
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# Prénom de naissance / Prénom utilisé : REGINA
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for m in re.finditer(r"Pr[ée]nom\s+(?:de\s+naissance|utilis[ée])\s*:\s*([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\s\-']+?)(?:\s*$)", full_text, re.MULTILINE):
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_add_name(m.group(1).strip())
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_add_name(m.group(1).strip(), "trackare_prenom", "high")
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# Lieu de naissance: BAYONNE, biarritz, 64102, 99999 → masquer comme VILLE
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for m in re.finditer(r"Lieu\s+de\s+naissance\s*:\s*(\S[^\n]*?)(?:\s*$)", full_text, re.MULTILINE):
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@@ -1798,19 +1878,19 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
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if len(val) > 3:
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hits.append(PiiHit(-1, "ADRESSE", val, PLACEHOLDERS["ADRESSE"]))
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# --- Pied de page : "Patient : NOM PRENOM - Date de naissance..." ---
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# --- Pied de page : "Patient : NOM PRENOM - Date de naissance..." (high context) ---
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for m in re.finditer(r"Patient\s*:\s*(.+?)\s*-\s*Date\s+de\s+naissance", full_text):
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_add_name(m.group(1).strip())
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_add_name(m.group(1).strip(), "trackare_patient_footer", "high")
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# --- Médecin courant (toutes occurrences) ---
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# --- Médecin courant (toutes occurrences) (medium context) ---
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for m in re.finditer(r"Médecin\s+courant\s*:\s*(?:DR\.?\s*)?(.+?)(?:\s*$)", full_text, re.MULTILINE):
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_add_name(m.group(1).strip())
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_add_name(m.group(1).strip(), "trackare_medecin_courant", "medium")
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# --- Médecin traitant (ligne après "Nom Adresse Téléphone") ---
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# --- Médecin traitant (ligne après "Nom Adresse Téléphone") (medium context) ---
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for m in re.finditer(r"Médecin\s+traitant\s*\n.*?Nom\s+Adresse\s+Téléphone\s*\n\s*(?:DR\.?\s*)?(.+?)(?:\d{5}|\s*$)", full_text, re.MULTILINE):
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_add_name(m.group(1).strip())
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_add_name(m.group(1).strip(), "trackare_medecin_traitant", "medium")
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# --- Contacts structurés ---
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# --- Contacts structurés (medium context) ---
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# Pattern: Relation NOM PRENOM [ADRESSE] [TEL]
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# Accepte les minuscules (Trackare écrit parfois "Conjoint vandestock michele")
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# Capture jusqu'à 3 tokens pour les noms composés (le moigne christophe)
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@@ -1825,6 +1905,7 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
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contact_parts = [g.strip(" .-'(),") for g in (m.group(1), m.group(2), m.group(3)) if g]
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# Ajouter chaque token >= 4 chars (pas les articles courts comme "le", "di", ni acronymes 3 lettres)
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for tok in contact_parts:
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_add_candidate(tok, "trackare_contact", "medium", False)
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if len(tok) >= 4 and tok.lower() not in _MEDICAL_STOP_WORDS_SET:
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names.add(tok)
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if tok[0].islower():
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@@ -1856,7 +1937,7 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
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if tok[0].islower():
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names.add(tok.capitalize())
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# --- Prescripteurs / Exécutants (trackare) ---
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# --- Prescripteurs / Exécutants (trackare) (medium context) ---
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for m in re.finditer(
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r"(?:Prescripteur|Prescrit\s+par|Exécut[ée]\s+par|Réalisé\s+par)\s*:?\s*"
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r"(?:(?:Dr|Pr)\.?\s+)?"
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@@ -1864,24 +1945,24 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
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r"(?:\s+([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-']+))?",
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full_text,
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):
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_add_name(m.group(1))
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_add_name(m.group(1), "trackare_prescripteur", "medium")
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if m.group(2):
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_add_name(m.group(2))
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_add_name(m.group(2), "trackare_prescripteur", "medium")
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# --- Médecins urgences (IAO, prise en charge, décision) ---
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# --- Médecins urgences (IAO, prise en charge, décision) (medium context) ---
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for m in re.finditer(r"IAO\s+([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-]+)", full_text):
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_add_name(m.group(1))
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_add_name(m.group(1), "trackare_iao", "medium")
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for m in re.finditer(
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r"Médecin\s+de\s+la\s+(?:prise\s+en\s+charge|décision)\s+médicale\s+"
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r"([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-]+)"
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r"(?:\s+([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-]+))?",
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full_text,
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):
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_add_name(m.group(1))
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_add_name(m.group(1), "trackare_medecin_urgences", "medium")
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if m.group(2):
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_add_name(m.group(2))
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_add_name(m.group(2), "trackare_medecin_urgences", "medium")
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# --- Noms soignants dans les Notes d'évolution / Notes IDE / Notes médicales ---
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# --- Noms soignants dans les Notes d'évolution / Notes IDE / Notes médicales (low context) ---
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# Pattern: "Note IDE\nPrenom NOM" ou "Note d'évolution\nPrenom NOM"
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for m in re.finditer(
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r"Note\s+(?:IDE|AS|d'[ée]volution|m[ée]dicale|kin[ée])\s*\n\s*"
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@@ -1890,22 +1971,26 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
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full_text
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):
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prenom, nom = m.group(1), m.group(2)
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_add_candidate(prenom, "trackare_note_ide_newline", "low", False)
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_add_candidate(nom, "trackare_note_ide_newline", "low", False)
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if prenom.lower() not in _MEDICAL_STOP_WORDS_SET:
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_add_name(prenom)
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if nom.lower() not in _MEDICAL_STOP_WORDS_SET:
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_add_name(nom)
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# --- Noms soignants multi-lignes : "Prénom\nNOM" dans les tableaux de prescriptions/soins ---
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# --- Noms soignants multi-lignes : "Prénom\nNOM" dans les tableaux de prescriptions/soins (low context) ---
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for m in re.finditer(
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r'\b([A-ZÉÈÀÙÂÊÎÔÛ][a-zéèàùâêîôûäëïöüç]{3,})\s*\n\s*([A-ZÉÈÀÙÂÊÎÔÛ]{4,})\b',
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full_text
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):
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prenom, nom = m.group(1), m.group(2)
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_add_candidate(prenom, "trackare_prenom_nom_multiline", "low", False)
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_add_candidate(nom, "trackare_prenom_nom_multiline", "low", False)
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if prenom.lower() not in _MEDICAL_STOP_WORDS_SET and nom.lower() not in _MEDICAL_STOP_WORDS_SET:
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_add_name(prenom)
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_add_name(nom)
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# --- Noms soignants sur la même ligne que "Note d'évolution" (ex: "Note d'évolution LACLAU-") ---
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# --- Noms soignants sur la même ligne que "Note d'évolution" (ex: "Note d'évolution LACLAU-") (low context) ---
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for m in re.finditer(
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r"Note[ \t]+(?:IDE|AS|d'[ée]volution|m[ée]dicale|kin[ée])[ \t]+"
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r"(?:DR\.?[ \t]+)?"
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@@ -1917,9 +2002,9 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
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if g:
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tok = g.rstrip('-')
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if len(tok) >= 4:
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_add_name_force(tok)
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_add_name_force(tok, "trackare_note_ide_inline", "low")
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# --- "Signé" suivi directement d'un nom de soignant (ex: "Signé LARRIEU-") ---
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# --- "Signé" suivi directement d'un nom de soignant (ex: "Signé LARRIEU-") (low context) ---
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# IMPORTANT: [ \t]+ (pas \s+) pour éviter de capturer les médicaments sur la ligne suivante
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for m in re.finditer(
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r"Signé[ \t]+(?!—|par\b)([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]+)"
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@@ -1930,9 +2015,9 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
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if g:
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tok = g.rstrip('-')
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if len(tok) >= 4:
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_add_name_force(tok)
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_add_name_force(tok, "trackare_signe_direct", "low")
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# --- "Signé —" + médicament + nom soignant (ex: "Signé — PARACETAMOL BBM 1000 MG INJ NARZABAL") ---
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# --- "Signé —" + médicament + nom soignant (ex: "Signé — PARACETAMOL BBM 1000 MG INJ NARZABAL") (low context) ---
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for m in re.finditer(
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r"Signé[ \t]+—[ \t]+.*(?:INJ|COMP|GEL|PDR|SOL|PERF|SUSP|CAPS|CREM|SACHET|SIROP)[ \t]+[-]?[ \t]*"
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r"([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]{3,})"
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@@ -1942,20 +2027,22 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
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for g in (m.group(1), m.group(2)):
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if g:
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tok = g.rstrip('-')
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_add_candidate(tok, "trackare_signe_med", "low", False)
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if len(tok) >= 4 and tok.lower() not in _MEDICAL_STOP_WORDS_SET:
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_add_name(tok)
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# --- Noms soignants après conditionnement médicament (ex: "Flacon(s) LACROUTS") ---
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# --- Noms soignants après conditionnement médicament (ex: "Flacon(s) LACROUTS") (low context) ---
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for m in re.finditer(
|
||||
r"(?:Flacon|Ampoule|Seringue|Poche|Comprim[ée]|Gélule|Sachet)(?:\(s\))?[ \t]+"
|
||||
r"([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]{3,})",
|
||||
full_text
|
||||
):
|
||||
tok = m.group(1).rstrip('-')
|
||||
_add_candidate(tok, "trackare_flacon", "low", False)
|
||||
if len(tok) >= 4 and tok.lower() not in _MEDICAL_STOP_WORDS_SET:
|
||||
_add_name(tok)
|
||||
|
||||
# --- "DR." / "DR" suivi d'un prénom seul (ex: "DR. Ute", "DR. Tam") dans les prescriptions ---
|
||||
# --- "DR." / "DR" suivi d'un prénom seul (ex: "DR. Ute", "DR. Tam") dans les prescriptions (medium context) ---
|
||||
for m in re.finditer(
|
||||
r"DR\.?[ \t]+([A-ZÉÈÀÙÂÊÎÔÛ][a-zéèàùâêîôûäëïöüç]{3,})"
|
||||
r"(?:[ \t]+([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]+))?",
|
||||
@@ -1965,9 +2052,9 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
|
||||
if g:
|
||||
tok = g.strip()
|
||||
if len(tok) >= 4:
|
||||
_add_name_force(tok)
|
||||
_add_name_force(tok, "trackare_dr", "medium")
|
||||
|
||||
# --- Noms soignants après timestamps dans activités de soins (ex: "07:00 ETCHEBARNE") ---
|
||||
# --- Noms soignants après timestamps dans activités de soins (ex: "07:00 ETCHEBARNE") (low context) ---
|
||||
# Format Trackare : actions de soins suivies de "HH:MM NOM" ou "HH : MM NOM"
|
||||
# Pattern restrictif : nom ALL-CAPS de 4+ lettres, filtre stop words (pattern bruyant)
|
||||
for m in re.finditer(
|
||||
@@ -1979,6 +2066,7 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
|
||||
for g in (m.group(1), m.group(2)):
|
||||
if g:
|
||||
tok = g.rstrip('-')
|
||||
_add_candidate(tok, "trackare_timestamp", "low", False)
|
||||
if len(tok) >= 4 and tok.lower() not in _MEDICAL_STOP_WORDS_SET:
|
||||
_add_name(tok)
|
||||
|
||||
@@ -1996,18 +2084,30 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
|
||||
continue
|
||||
filtered.add(tok)
|
||||
|
||||
return filtered, hits, force_names
|
||||
return filtered, hits, force_names, candidates
|
||||
|
||||
|
||||
def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, set]:
|
||||
def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, set, List[NameCandidate]]:
|
||||
"""Pré-scan du document brut pour extraire les noms de personnes
|
||||
depuis les champs structurés (Patient, Rédigé par, etc.).
|
||||
Retourne (names, force_names) : ensemble de tokens à masquer,
|
||||
et sous-ensemble qui bypass les stop words."""
|
||||
Retourne (names, force_names, candidates) : ensemble de tokens à masquer,
|
||||
sous-ensemble qui bypass les stop words, et liste de NameCandidate
|
||||
avec métadonnées de confiance pour la validation croisée NER-first."""
|
||||
wl_sections = set((cfg.get("whitelist", {}) or {}).get("sections_titres", []) or [])
|
||||
wl_phrases = set((cfg.get("whitelist", {}) or {}).get("noms_maj_excepts", []) or [])
|
||||
names: set = set()
|
||||
force_names: set = set()
|
||||
candidates: List[NameCandidate] = []
|
||||
|
||||
def _add_candidate(token: str, source: str, strength: str, bypass: bool):
|
||||
"""Ajoute un NameCandidate à la liste (dédupliqué par token+source)."""
|
||||
token = token.strip(" .-'")
|
||||
if len(token) < 4:
|
||||
return
|
||||
candidates.append(NameCandidate(
|
||||
token=token, source=source,
|
||||
context_strength=strength, bypass_stopwords=bypass,
|
||||
))
|
||||
|
||||
def _add_compound(match_str: str):
|
||||
"""Ajoute le nom composé complet en plus des tokens individuels (DI LULLO, LE MOIGNE)."""
|
||||
@@ -2017,7 +2117,7 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, s
|
||||
if len(compound) >= 5:
|
||||
names.add(compound)
|
||||
|
||||
def _add_tokens(match_str: str):
|
||||
def _add_tokens(match_str: str, _cand_source: str = "", _cand_strength: str = "medium"):
|
||||
_add_compound(match_str)
|
||||
for token in match_str.split():
|
||||
token = token.strip(" .-'")
|
||||
@@ -2025,11 +2125,13 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, s
|
||||
continue
|
||||
if token.upper() in wl_sections or token in wl_phrases:
|
||||
continue
|
||||
if _cand_source:
|
||||
_add_candidate(token, _cand_source, _cand_strength, False)
|
||||
if token.lower() in _MEDICAL_STOP_WORDS_SET:
|
||||
continue
|
||||
names.add(token)
|
||||
|
||||
def _add_tokens_force_all(match_str: str):
|
||||
def _add_tokens_force_all(match_str: str, _cand_source: str = "", _cand_strength: str = "high"):
|
||||
"""Bypass stop words pour TOUS les tokens (contexte Patient: très fiable)."""
|
||||
_add_compound(match_str)
|
||||
for token in match_str.split():
|
||||
@@ -2038,10 +2140,12 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, s
|
||||
continue
|
||||
if token.upper() in wl_sections or token in wl_phrases:
|
||||
continue
|
||||
if _cand_source:
|
||||
_add_candidate(token, _cand_source, _cand_strength, True)
|
||||
names.add(token)
|
||||
force_names.add(token)
|
||||
|
||||
def _add_tokens_force_first(match_str):
|
||||
def _add_tokens_force_first(match_str, _cand_source: str = "", _cand_strength: str = "medium"):
|
||||
"""Comme _add_tokens mais force TOUS les tokens (contexte Dr/Mme fort).
|
||||
|
||||
Après Dr/Mme, tous les tokens sont des noms — même s'ils sont
|
||||
@@ -2055,54 +2159,58 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, s
|
||||
continue
|
||||
if token.upper() in wl_sections or token in wl_phrases:
|
||||
continue
|
||||
if _cand_source:
|
||||
_add_candidate(token, _cand_source, _cand_strength, True)
|
||||
names.add(token)
|
||||
force_names.add(token)
|
||||
|
||||
# --- high context: DPI structured fields (near-certain patient identity) ---
|
||||
for m in RE_EXTRACT_PATIENT.finditer(full_text):
|
||||
_add_tokens_force_all(m.group(1))
|
||||
for m in RE_EXTRACT_REDIGE.finditer(full_text):
|
||||
_add_tokens(m.group(1))
|
||||
for m in RE_EXTRACT_MME_MR.finditer(full_text):
|
||||
_add_tokens_force_first(m.group(1))
|
||||
for m in RE_EXTRACT_DR_DEST.finditer(full_text):
|
||||
_add_tokens_force_first(m.group(1))
|
||||
_add_tokens_force_all(m.group(1), "RE_EXTRACT_PATIENT", "high")
|
||||
# Champs d'identité structurés (trackare / DPI)
|
||||
for m in RE_EXTRACT_NOM_NAISSANCE.finditer(full_text):
|
||||
_add_tokens_force_all(m.group(1))
|
||||
_add_tokens_force_all(m.group(1), "RE_EXTRACT_NOM_NAISSANCE", "high")
|
||||
for m in RE_EXTRACT_NOM_UTILISE.finditer(full_text):
|
||||
_add_tokens_force_all(m.group(1))
|
||||
_add_tokens_force_all(m.group(1), "RE_EXTRACT_NOM_UTILISE", "high")
|
||||
for m in RE_EXTRACT_NOM_PRENOM.finditer(full_text):
|
||||
_add_tokens_force_all(m.group(1))
|
||||
_add_tokens_force_all(m.group(1), "RE_EXTRACT_NOM_PRENOM", "high")
|
||||
for m in RE_EXTRACT_PRENOM.finditer(full_text):
|
||||
_add_tokens_force_all(m.group(1))
|
||||
for m in RE_EXTRACT_LIEU_NAISSANCE.finditer(full_text):
|
||||
_add_tokens(m.group(1))
|
||||
for m in RE_EXTRACT_VILLE_RESIDENCE.finditer(full_text):
|
||||
_add_tokens(m.group(1))
|
||||
# Contacts structurés (conjoint, concubin, etc.)
|
||||
for m in RE_EXTRACT_CONTACT.finditer(full_text):
|
||||
_add_tokens(m.group(1))
|
||||
if m.group(2):
|
||||
_add_tokens(m.group(2))
|
||||
# Personnel médical avec rôle (Aide, Cadre Infirmier, Prescripteur, etc.)
|
||||
for m in RE_EXTRACT_STAFF_ROLE.finditer(full_text):
|
||||
_add_tokens(m.group(1))
|
||||
# Pr / Professeur + nom(s)
|
||||
for m in RE_EXTRACT_PR.finditer(full_text):
|
||||
_add_tokens_force_first(m.group(1))
|
||||
# Opérateur / Anesthésiste / Chirurgien + nom(s)
|
||||
for m in RE_EXTRACT_OPERATEUR.finditer(full_text):
|
||||
_add_tokens_force_first(m.group(1))
|
||||
# Nom de cabinet (ex: "CABINET ETXEBARNONDOA")
|
||||
for m in RE_EXTRACT_CABINET.finditer(full_text):
|
||||
_add_tokens(m.group(1))
|
||||
_add_tokens_force_all(m.group(1), "RE_EXTRACT_PRENOM", "high")
|
||||
# En-tête "Courrier Epi - NOM, PRENOM" (lettres de sortie)
|
||||
for m in RE_EXTRACT_COURRIER.finditer(full_text):
|
||||
# Format "NOM, PRENOM" : chaque partie est un token de nom
|
||||
for part in m.group(1).split(","):
|
||||
part = part.strip()
|
||||
if part:
|
||||
_add_tokens_force_all(part)
|
||||
_add_tokens_force_all(part, "RE_EXTRACT_COURRIER", "high")
|
||||
|
||||
# --- medium context: medical titles (Dr, Mme, Pr, Opérateur, etc.) ---
|
||||
for m in RE_EXTRACT_REDIGE.finditer(full_text):
|
||||
_add_tokens(m.group(1), "RE_EXTRACT_REDIGE", "medium")
|
||||
for m in RE_EXTRACT_MME_MR.finditer(full_text):
|
||||
_add_tokens_force_first(m.group(1), "RE_EXTRACT_MME_MR", "medium")
|
||||
for m in RE_EXTRACT_DR_DEST.finditer(full_text):
|
||||
_add_tokens_force_first(m.group(1), "RE_EXTRACT_DR_DEST", "medium")
|
||||
for m in RE_EXTRACT_LIEU_NAISSANCE.finditer(full_text):
|
||||
_add_tokens(m.group(1), "RE_EXTRACT_LIEU_NAISSANCE", "medium")
|
||||
for m in RE_EXTRACT_VILLE_RESIDENCE.finditer(full_text):
|
||||
_add_tokens(m.group(1), "RE_EXTRACT_VILLE_RESIDENCE", "medium")
|
||||
# Contacts structurés (conjoint, concubin, etc.)
|
||||
for m in RE_EXTRACT_CONTACT.finditer(full_text):
|
||||
_add_tokens(m.group(1), "RE_EXTRACT_CONTACT", "medium")
|
||||
if m.group(2):
|
||||
_add_tokens(m.group(2), "RE_EXTRACT_CONTACT", "medium")
|
||||
# Personnel médical avec rôle (Aide, Cadre Infirmier, Prescripteur, etc.)
|
||||
for m in RE_EXTRACT_STAFF_ROLE.finditer(full_text):
|
||||
_add_tokens(m.group(1), "RE_EXTRACT_STAFF_ROLE", "medium")
|
||||
# Pr / Professeur + nom(s)
|
||||
for m in RE_EXTRACT_PR.finditer(full_text):
|
||||
_add_tokens_force_first(m.group(1), "RE_EXTRACT_PR", "medium")
|
||||
# Opérateur / Anesthésiste / Chirurgien + nom(s)
|
||||
for m in RE_EXTRACT_OPERATEUR.finditer(full_text):
|
||||
_add_tokens_force_first(m.group(1), "RE_EXTRACT_OPERATEUR", "medium")
|
||||
# Nom de cabinet (ex: "CABINET ETXEBARNONDOA")
|
||||
for m in RE_EXTRACT_CABINET.finditer(full_text):
|
||||
_add_tokens(m.group(1), "RE_EXTRACT_CABINET", "medium")
|
||||
|
||||
# Extraction des noms dans les listes virgulées après Dr/Docteur ou Mmes/Mme
|
||||
# ex: "le Dr DUVAL, MACHELART, CHARLANNE, LAZARO, il a été proposé"
|
||||
@@ -2117,6 +2225,7 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, s
|
||||
continue
|
||||
if tok.upper() in wl_sections or tok in wl_phrases:
|
||||
continue
|
||||
_add_candidate(tok, "RE_DR_COMMA_LIST", "medium", False)
|
||||
if tok.lower() in _MEDICAL_STOP_WORDS_SET:
|
||||
continue
|
||||
names.add(tok)
|
||||
@@ -2138,6 +2247,7 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, s
|
||||
continue
|
||||
if tok.upper() in wl_sections or tok in wl_phrases:
|
||||
continue
|
||||
_add_candidate(tok, "RE_CIVILITE_COMMA_LIST", "medium", False)
|
||||
if tok.lower() in _MEDICAL_STOP_WORDS_SET:
|
||||
continue
|
||||
names.add(tok)
|
||||
@@ -2150,6 +2260,7 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, s
|
||||
for m in _RE_DR_INITIAL_DOT_NAME.finditer(full_text):
|
||||
names.add(m.group(2)) # Le nom (ELLIE)
|
||||
force_names.add(m.group(2))
|
||||
_add_candidate(m.group(2), "DR_INITIAL_DOT_NAME", "medium", True)
|
||||
# Ajouter aussi "X.NOM" complet pour le raster (token collé)
|
||||
names.add(f"{m.group(1)}.{m.group(2)}")
|
||||
force_names.add(f"{m.group(1)}.{m.group(2)}")
|
||||
@@ -2163,7 +2274,7 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, s
|
||||
re.MULTILINE
|
||||
)
|
||||
for m in _RE_EMAIL_HEADER.finditer(full_text):
|
||||
_add_tokens_force_all(m.group(1))
|
||||
_add_tokens_force_all(m.group(1), "EMAIL_HEADER", "medium")
|
||||
|
||||
# Pour les noms composés avec tiret (ex: "LACLAU-LACROUTS"),
|
||||
# ajouter aussi les parties individuelles pour capturer les occurrences standalone.
|
||||
@@ -2179,7 +2290,87 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, s
|
||||
names.add(part)
|
||||
force_names.add(part)
|
||||
|
||||
return names, force_names
|
||||
return names, force_names, candidates
|
||||
|
||||
|
||||
def _cross_validate_name_candidates(
|
||||
candidates: List[NameCandidate],
|
||||
ner_detections: List["NerDetection"],
|
||||
insee_noms: set,
|
||||
insee_prenoms: set,
|
||||
medical_stopwords: set,
|
||||
) -> Tuple[set, set]:
|
||||
"""Valide les candidats de noms extraits par regex en croisant avec les détections NER
|
||||
et les gazetteers INSEE.
|
||||
|
||||
Matrice de décision :
|
||||
- high context → toujours accepter (champs DPI structurés, quasi-certains)
|
||||
- medium context + NER confirmé → accepter
|
||||
- medium context + non NER + INSEE + non stopword → accepter
|
||||
- medium context + non NER + non INSEE + stopword → REJETER
|
||||
- medium context + non NER + non INSEE + non stopword → accepter (bénéfice du doute)
|
||||
- low context + NER confirmé → accepter
|
||||
- low context + non NER + INSEE + non stopword → accepter
|
||||
- low context + non NER + stopword → REJETER
|
||||
- low context + non NER + non INSEE → REJETER
|
||||
|
||||
Un nom est "NER confirmé" si une NerDetection a un token correspondant (case-insensitive).
|
||||
|
||||
Returns:
|
||||
(validated_names, validated_force_names) : ensembles de tokens validés.
|
||||
"""
|
||||
# Construire le set de tokens confirmés par NER (uppercase sans accents, pour matching)
|
||||
ner_confirmed_tokens: set = set()
|
||||
for det in ner_detections:
|
||||
ner_confirmed_tokens.add(_normalize_nfkd_upper(det.token))
|
||||
|
||||
validated_names: set = set()
|
||||
validated_force_names: set = set()
|
||||
|
||||
for cand in candidates:
|
||||
tok = cand.token
|
||||
tok_upper = _normalize_nfkd_upper(tok)
|
||||
tok_lower = tok.lower()
|
||||
|
||||
is_ner_confirmed = tok_upper in ner_confirmed_tokens
|
||||
is_in_insee = tok_upper in insee_noms or tok_upper in insee_prenoms
|
||||
is_stopword = tok_lower in medical_stopwords
|
||||
|
||||
strength = cand.context_strength
|
||||
|
||||
accepted = False
|
||||
|
||||
if strength == "high":
|
||||
# Toujours accepter les champs DPI structurés
|
||||
accepted = True
|
||||
elif strength == "medium":
|
||||
if is_ner_confirmed:
|
||||
accepted = True
|
||||
elif is_in_insee and not is_stopword:
|
||||
accepted = True
|
||||
elif not is_in_insee and is_stopword:
|
||||
accepted = False # REJETER
|
||||
else:
|
||||
# non NER + non INSEE + non stopword → bénéfice du doute
|
||||
accepted = True
|
||||
elif strength == "low":
|
||||
if is_ner_confirmed:
|
||||
accepted = True
|
||||
elif is_in_insee and not is_stopword:
|
||||
accepted = True
|
||||
elif is_stopword:
|
||||
accepted = False # REJETER
|
||||
elif not is_in_insee:
|
||||
accepted = False # REJETER
|
||||
else:
|
||||
accepted = False
|
||||
|
||||
if accepted:
|
||||
validated_names.add(tok)
|
||||
if cand.bypass_stopwords:
|
||||
validated_force_names.add(tok)
|
||||
|
||||
return validated_names, validated_force_names
|
||||
|
||||
|
||||
def _apply_extracted_names(text: str, names: set, audit: List[PiiHit], force_names: set = None) -> str:
|
||||
@@ -2262,15 +2453,17 @@ def anonymise_document_regex(pages_text: List[str], tables_lines: List[List[str]
|
||||
full_raw = "\n".join(pages_text) + "\n" + "\n".join(
|
||||
"\n".join(rows) for rows in tables_lines
|
||||
)
|
||||
extracted_names, doc_force_names = _extract_document_names(full_raw, cfg)
|
||||
extracted_names, doc_force_names, doc_candidates = _extract_document_names(full_raw, cfg)
|
||||
|
||||
# Phase 0b : si document Trackare, extraction renforcée des PII structurés
|
||||
is_trackare = _is_trackare_document(full_raw)
|
||||
trackare_force_names: set = set()
|
||||
all_candidates: List[NameCandidate] = list(doc_candidates)
|
||||
if is_trackare:
|
||||
trackare_names, trackare_hits, trackare_force_names = _extract_trackare_identity(full_raw)
|
||||
trackare_names, trackare_hits, trackare_force_names, trackare_candidates = _extract_trackare_identity(full_raw)
|
||||
extracted_names.update(trackare_names)
|
||||
audit.extend(trackare_hits)
|
||||
all_candidates.extend(trackare_candidates)
|
||||
# Fusionner les force_names des deux sources
|
||||
all_force_names = doc_force_names | trackare_force_names
|
||||
|
||||
@@ -2570,6 +2763,131 @@ def apply_eds_pseudo_on_narrative(text_out: str, cfg: Dict[str, Any], manager: "
|
||||
final = "".join(rebuilt_list)
|
||||
return final, hits
|
||||
|
||||
|
||||
# ----------------- NER-first: run NER on original (unmasked) text -----------------
|
||||
|
||||
def _run_ner_on_original_text(
|
||||
pages_text: List[str],
|
||||
eds_pseudo_mgr=None,
|
||||
gliner_mgr=None,
|
||||
camembert_mgr=None,
|
||||
cfg: Dict[str, Any] = None,
|
||||
) -> List[NerDetection]:
|
||||
"""Exécute les modèles NER disponibles sur le texte original (non masqué)
|
||||
et retourne une liste dédupliquée de NerDetection.
|
||||
|
||||
Cette fonction est conçue pour l'architecture NER-first : on fait tourner
|
||||
les NER *avant* le masquage regex, afin de pouvoir valider les candidats
|
||||
de noms extraits par regex avec les signaux NER.
|
||||
|
||||
Paramètres:
|
||||
pages_text: texte original par page (non masqué)
|
||||
eds_pseudo_mgr: instance EdsPseudoManager (optionnelle)
|
||||
gliner_mgr: instance GlinerManager (optionnelle)
|
||||
camembert_mgr: instance CamembertNerManager (optionnelle)
|
||||
cfg: configuration (non utilisée pour l'instant, réservée)
|
||||
|
||||
Returns:
|
||||
Liste de NerDetection dédupliquée (par token+label+page+source).
|
||||
"""
|
||||
detections: List[NerDetection] = []
|
||||
seen: set = set() # (token_lower, label, page_idx, source) pour dédoublonnage
|
||||
|
||||
def _add_detection(token: str, label: str, score: float, page_idx: int, source: str):
|
||||
"""Ajoute une détection si non déjà vue."""
|
||||
key = (token.lower(), label, page_idx, source)
|
||||
if key not in seen:
|
||||
seen.add(key)
|
||||
detections.append(NerDetection(
|
||||
token=token, label=label, score=score,
|
||||
page_idx=page_idx, source=source,
|
||||
))
|
||||
|
||||
# Mapping des labels NER vers labels normalisés
|
||||
_EDS_LABEL_NORM = {
|
||||
"NOM": "NOM", "PRENOM": "PRENOM",
|
||||
"HOPITAL": "HOPITAL", "VILLE": "VILLE",
|
||||
"ADRESSE": "LOC", "ZIP": "LOC",
|
||||
}
|
||||
_GLINER_LABEL_NORM = {
|
||||
"person_name": "NOM", "hospital": "HOPITAL",
|
||||
"city": "VILLE", "postal_address": "LOC",
|
||||
}
|
||||
_CAMEMBERT_LABEL_NORM = {
|
||||
"PER": "NOM", "HOPITAL": "HOPITAL",
|
||||
"VILLE": "VILLE", "ADRESSE": "LOC", "ZIP": "LOC",
|
||||
}
|
||||
|
||||
for page_idx, page_text in enumerate(pages_text):
|
||||
if not page_text.strip():
|
||||
continue
|
||||
|
||||
# Découper en paragraphes (comme apply_eds_pseudo_on_narrative)
|
||||
paras = [p for p in re.split(r"\n\s*\n", page_text) if p.strip()]
|
||||
|
||||
# --- EDS-Pseudo ---
|
||||
if eds_pseudo_mgr is not None and hasattr(eds_pseudo_mgr, 'infer_paragraphs') and eds_pseudo_mgr.is_loaded():
|
||||
try:
|
||||
ents_per_para = eds_pseudo_mgr.infer_paragraphs(paras)
|
||||
for para_ents in ents_per_para:
|
||||
for ent in para_ents:
|
||||
raw_label = ent.get("entity_group", "")
|
||||
norm_label = _EDS_LABEL_NORM.get(raw_label)
|
||||
if norm_label:
|
||||
_add_detection(
|
||||
token=ent.get("word", ""),
|
||||
label=norm_label,
|
||||
score=ent.get("score", 0.0),
|
||||
page_idx=page_idx,
|
||||
source="eds_pseudo",
|
||||
)
|
||||
except Exception as e:
|
||||
log.warning(f"_run_ner_on_original_text: EDS-Pseudo erreur page {page_idx}: {e}")
|
||||
|
||||
# --- GLiNER ---
|
||||
if gliner_mgr is not None and hasattr(gliner_mgr, 'predict') and gliner_mgr.is_loaded():
|
||||
try:
|
||||
# GLiNER sur le texte complet de la page (pas par paragraphe)
|
||||
gliner_ents = gliner_mgr.predict(page_text, threshold=0.4)
|
||||
for ent in gliner_ents:
|
||||
raw_label = ent.get("label", "")
|
||||
norm_label = _GLINER_LABEL_NORM.get(raw_label)
|
||||
if norm_label:
|
||||
_add_detection(
|
||||
token=ent.get("text", ""),
|
||||
label=norm_label,
|
||||
score=ent.get("score", 0.0),
|
||||
page_idx=page_idx,
|
||||
source="gliner",
|
||||
)
|
||||
except Exception as e:
|
||||
log.warning(f"_run_ner_on_original_text: GLiNER erreur page {page_idx}: {e}")
|
||||
|
||||
# --- CamemBERT-bio ---
|
||||
if camembert_mgr is not None and hasattr(camembert_mgr, 'predict_long') and camembert_mgr.is_loaded():
|
||||
try:
|
||||
cam_ents = camembert_mgr.predict_long(page_text, threshold=0.4)
|
||||
for ent in cam_ents:
|
||||
raw_label = ent.get("label", "")
|
||||
norm_label = _CAMEMBERT_LABEL_NORM.get(raw_label)
|
||||
if norm_label:
|
||||
_add_detection(
|
||||
token=ent.get("word", ""),
|
||||
label=norm_label,
|
||||
score=ent.get("score", 0.0),
|
||||
page_idx=page_idx,
|
||||
source="camembert_bio",
|
||||
)
|
||||
except Exception as e:
|
||||
log.warning(f"_run_ner_on_original_text: CamemBERT-bio erreur page {page_idx}: {e}")
|
||||
|
||||
log.info(f"NER-first: {len(detections)} détections sur {len(pages_text)} pages "
|
||||
f"(eds={sum(1 for d in detections if d.source == 'eds_pseudo')}, "
|
||||
f"gliner={sum(1 for d in detections if d.source == 'gliner')}, "
|
||||
f"camembert={sum(1 for d in detections if d.source == 'camembert_bio')})")
|
||||
return detections
|
||||
|
||||
|
||||
# ----------------- FINESS Aho-Corasick establishment matching -----------------
|
||||
|
||||
def _build_finess_ac():
|
||||
|
||||
Reference in New Issue
Block a user