fix(phase2): Élimination FP cross-line + word boundaries — 0 fuite, 0 FP médical

- Remplace \s+ par [ \t]+ dans 11 regex d'extraction de noms (empêche capture cross-line de médicaments)
- Ajoute \b word boundaries dans RE_PERSON_CONTEXT (empêche "PDR" de matcher "DR")
- Ajoute filtrage _MEDICAL_STOP_WORDS_SET dans selective_rescan._rescan_person
- Ajoute stop words : labos pharma (MYL/VTS/ARW/PAN/MSO), dosages (FAIBLE/FORT), anatomie imagerie (CEREBRAL/ABDOMINO-PELVIEN)
- Filtre stop words dans _add_name_force et _add_tokens_force_first
- Mise à jour baseline regression_tests/ avec 29 fichiers du batch audit 30

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

anonymizer_core_refactored_onnx.py

@@ -191,7 +191,8 @@ RE_EMAIL = re.compile(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}")
 RE_TEL = re.compile(r"(?<!\d)(?:\+33\s?|0)\d(?:[\s.\-]?\d){8}(?!\d)")
 RE_TEL_COMPACT = re.compile(r"(?<!\d)0[1-9]\d{8}(?!\d)")
 RE_IBAN = re.compile(r"\b[A-Z]{2}\d{2}(?:\s?[A-Z0-9]{4}){3,7}(?:\s?[A-Z0-9]{1,4})\b")
-RE_IPP = re.compile(r"\bIPP\s*[:\-]?\s*([A-Za-z0-9]{6,})\b", re.IGNORECASE)
+RE_IPP = re.compile(r"\b(?:I\.?P\.?P\.?|IPP|N°\s*Ipp)\s*[:\-]?\s*([A-Za-z0-9]{6,})\b", re.IGNORECASE)
+RE_CSULT = re.compile(r"\b(?:N°\s*Csult|N°\s*Interv)\s*[:\-]?\s*(\d{6,})\b", re.IGNORECASE)
 RE_FINESS = re.compile(r"\b(?:N°\s*)?FINESS?\s*[:\-]?\s*(\d{9})\b", re.IGNORECASE)
 RE_OGC = re.compile(r"\b(?:N°\s*)?OGC\s*[:\-]?\s*([A-Za-z0-9\-]{1,})\b", re.IGNORECASE)
 RE_RPPS = re.compile(r"\b(?:N°\s*)?RPPS\s*[:\-]?\s*(\d{8,11})\b", re.IGNORECASE)
@@ -294,8 +295,12 @@ _MEDICAL_STOP_WORDS_SET = {
     "meropenem", "imipenem", "clindamycine", "doxycycline",
     "azithromycine", "clarithromycine", "cotrimoxazole", "bactrim",
     "polyionique", "propranolol", "apidra", "solostar",
-    # Suffixes laboratoires pharmaceutiques
+    # Noms et suffixes laboratoires pharmaceutiques
     "arw", "myl", "myp", "arg", "teva", "bga", "agt",
+    "mylan", "biogaran", "arrow", "sandoz", "zentiva", "cristers",
+    "accord", "viatris", "ranbaxy", "ratiopharm", "almus", "qualimed",
+    "evolugen", "alter", "zydus", "medisol", "substipharm",
+    "sdz", "bgr", "egt", "rnb",
     # Formes galéniques / voies d'administration
     "cpr", "sachet", "orale", "oral", "sol", "buv", "stylo", "flexpen",
     "flestouch", "kwikpen", "inj", "susp", "gelule", "comprime",
@@ -374,6 +379,9 @@ _MEDICAL_STOP_WORDS_SET = {
     "montelukast", "rosuvastatine",
     # Abréviations pharma courtes
     "cpr", "sol", "bic", "agt", "poche", "inhal",
+    # Termes chirurgicaux/cliniques FP
+    "cure", "endocrine", "operatoire", "opératoire", "realisee", "réalisée",
+    "gauche", "droit", "droite", "face", "profil",
     # Faux positifs EDS supplémentaires
     "psy", "inhales", "inhalés", "kwikpen", "lansoprazole", "tiorfan", "smecta",
     "axa", "ttt", "anionique", "abdomino", "cod", "omi", "urg", "med",
@@ -444,6 +452,20 @@ _MEDICAL_STOP_WORDS_SET = {
     "thermie", "alim", "alimentation", "admin",
     # Médicaments/tests labo capturés par patterns soignants
     "biprofenid", "bi-profenid", "phosphatase", "phosphatases",
+    "ecbu", "suppo", "suppositoire", "mucite", "microlax", "normacol",
+    "ciprofloxacine", "lavement", "desinfection", "désinfection",
+    "avaler", "rachis", "lombaire", "thoraco-lombaire",
+    "cérébrale", "cerebrale", "cérébral", "cerebral", "hépatique", "hepatique",
+    "thoracique", "abdominale", "abdominal", "pelvienne", "pelvien", "médullaire",
+    # Dosages et labos pharma (FP fréquents dans prescriptions Trackare)
+    "faible", "fort", "forte",
+    "myl", "mylan", "sandoz", "teva", "arrow", "biogaran", "zentiva", "eg",
+    "arw", "pan", "mso", "bgn", "ratiopharm", "accord", "vts", "viatris",
+    "abdomino-pelvien", "abdomino", "pelvien", "thoraco-abdominal",
+    "entree", "entrée", "continu", "continue",
+    "morphine", "claforan", "skenan", "actiskenan",
+    # Fragments de noms de médicaments (pdfplumber split)
+    "sium", "pegic", "fenid", "profenid",
     # Termes structurels trackare
     "transmissions", "transmission", "releve", "relevé",
     "objectif", "objectifs", "evaluation", "évaluation",
@@ -507,11 +529,11 @@ _MEDICAL_STOP_WORDS = (
 # Un token de nom : commence par majuscule, lettres/tirets/apostrophes (PAS d'espace ni de point)
 _PERSON_TOKEN = r"[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\']+"
 RE_PERSON_CONTEXT = re.compile(
-    r"(?:(?:Dr\.?|DR\.?|Docteur|Pr\.?|Professeur|Mme|MME|Madame|M\.|Mr\.?|Monsieur"
+    r"(?:(?:\bDr\.?|\bDR\.?|\bDocteur|\bPr\.?|\bProfesseur|\bMme|\bMME|\bMadame|\bM\.|\bMr\.?|\bMonsieur"
-    r"|Nom\s*:\s*"
+    r"|\bNom[ \t]*:[ \t]*"
-    r"|Rédigé\s+par|Validé\s+par|Signé\s+par|Saisi\s+par|Réalisé\s+par"
+    r"|\bRédigé[ \t]+par|\bValidé[ \t]+par|\bSigné[ \t]+par|\bSaisi[ \t]+par|\bRéalisé[ \t]+par"
-    r")\s+)"
+    r")[ \t]+)"
-    rf"({_PERSON_TOKEN}(?:\s+{_PERSON_TOKEN}){{0,2}})"  # Max 3 mots
+    rf"({_PERSON_TOKEN}(?:[ \t]+{_PERSON_TOKEN}){{0,2}})"  # Max 3 mots, pas de newline
 )
 
 # Noms en MAJUSCULES dans des listes virgulées (ex: "le Dr X, Y, LAZARO")
@@ -561,8 +583,8 @@ RE_EXTRACT_CONTACT = re.compile(
     r"(?:\s+([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\']+))?",
 )
 RE_EXTRACT_REDIGE = re.compile(
-    r"(?:Rédigé|Validé|Signé|Saisi)\s+par\s+"
+    r"(?:Rédigé|Validé|Signé|Saisi)[ \t]+par[ \t]+"
-    rf"((?:{_UC_NAME_TOKEN})(?:\s+(?:{_UC_NAME_TOKEN}))*)",
+    rf"((?:{_UC_NAME_TOKEN})(?:[ \t]+(?:{_UC_NAME_TOKEN})){{0,2}})",
 )
 # Token nom composé : JEAN-PIERRE, CAZELLES-BOUDIER, etc.
 _UC_COMPOUND = r"[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,}(?:-[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,})*"
@@ -573,22 +595,34 @@ RE_EXTRACT_MME_MR = re.compile(
 )
 _INITIAL_OPT = r"(?:[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]\.\s*(?:-?\s*[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]\.\s*)?)?"
 RE_EXTRACT_DR_DEST = re.compile(
-    r"(?:DR\.?|Dr\.?|Docteur)\s+"
+    r"(?:DR\.?|Dr\.?|Docteur)[ \t]+"
     + _INITIAL_OPT +
-    rf"((?:{_UC_NAME_TOKEN})(?:\s+(?:{_UC_NAME_TOKEN}))*)",
+    rf"((?:{_UC_NAME_TOKEN})(?:[ \t]+(?:{_UC_NAME_TOKEN})){{0,2}})",
 )
 # Noms du personnel médical après un rôle : "Aide : Marie-Paule BORDABERRY"
 RE_EXTRACT_STAFF_ROLE = re.compile(
-    r"(?:Aide|Infirmière?|IDE|IADE|IBODE|ASH?|Cadre\s+Infirmier"
+    r"(?:Aide|Infirmière?|IDE|IADE|IBODE|ASH?|Cadre[ \t]+Infirmier"
-    r"|Prescripteur|Prescrit\s+par|Exécut[ée]\s+par|Réalisé\s+par)\s*:?\s*"
+    r"|Prescripteur|Prescrit[ \t]+par|Exécut[ée][ \t]+par|Réalisé[ \t]+par)[ \t]*:?[ \t]*"
-    r"((?:[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][a-zéèàùâêîôûäëïöüç]+(?:\s*-\s*[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][a-zéèàùâêîôûäëïöüç]+)?\s+)?"
+    r"((?:[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][a-zéèàùâêîôûäëïöüç]+(?:[ \t]*-[ \t]*[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][a-zéèàùâêîôûäëïöüç]+)?[ \t]+)?"
-    r"(?:[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,}[\-]?)(?:[\s\-]+[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,}){0,2})",
+    r"(?:[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,}[\-]?)(?:[ \t\-]+[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,}){0,2})",
 )
 # "Pr DUVAL", "Pr. J.-M. DUVAL", "Professeur DUVAL"
 RE_EXTRACT_PR = re.compile(
-    r"(?:Pr\.?|Professeur)\s+"
+    r"(?:Pr\.?|Professeur)[ \t]+"
     + _INITIAL_OPT +
-    rf"((?:{_UC_NAME_TOKEN})(?:\s+(?:{_UC_NAME_TOKEN}))*)",
+    rf"((?:{_UC_NAME_TOKEN})(?:[ \t]+(?:{_UC_NAME_TOKEN})){{0,2}})",
+)
+# "Opérateur : Docteur X. NOM", "Anesthésiste(s) Docteur J. NOM",
+# "Opérateur : Dr J.-M. NOM", "Anesthésiste : NOM"
+RE_EXTRACT_OPERATEUR = re.compile(
+    r"(?:Op[ée]rateur|Anesth[ée]siste\(?s?\)?|Chirurgien)[ \t]*:?[ \t]*"
+    r"(?:(?:Docteur|Dr\.?|Pr\.?)[ \t]+)?"
+    + _INITIAL_OPT +
+    rf"((?:{_UC_COMPOUND})(?:[ \t]+(?:{_UC_COMPOUND})){{0,2}})",
+)
+# Téléphone avec extension slash : 05.59.44.38.32/34
+RE_TEL_SLASH = re.compile(
+    r"(?<!\d)(?:\+33\s?|0)\d(?:[\s.\-]?\d){8}(?:/\d{1,4})(?!\d)"
 )
 
 CID_PATTERN = re.compile(r"\(cid:\d+\)")
@@ -596,7 +630,7 @@ CID_PATTERN = re.compile(r"\(cid:\d+\)")
 # --- Nouvelles regex : dates, adresses, âges, dossiers ---
 _MOIS_FR = r"(?:janvier|février|mars|avril|mai|juin|juillet|août|septembre|octobre|novembre|décembre)"
 RE_DATE_NAISSANCE = re.compile(
-    r"(?:n[ée]+\s+le|date\s+de\s+naissance|DDN)\s*[:\-]?\s*"
+    r"(?:\bn[ée]+(?:\(?e?\)?)?\s+le|date\s+de\s+naissance|DDN)\s*[:\-]?\s*"
     r"(\d{1,2}[\s/.\-]\d{1,2}[\s/.\-]\d{2,4}|\d{1,2}\s+" + _MOIS_FR + r"\s+\d{4})",
     re.IGNORECASE,
 )
@@ -947,6 +981,10 @@ def _mask_admin_label(line: str, audit: List[PiiHit], page_idx: int) -> str:
     if m:
         val = m.group(1); audit.append(PiiHit(page_idx, "IPP", val, PLACEHOLDERS["IPP"]))
         return RE_IPP.sub(lambda _: f"IPP : {PLACEHOLDERS['IPP']}", line)
+    m = RE_CSULT.search(line)
+    if m:
+        val = m.group(1); audit.append(PiiHit(page_idx, "DOSSIER", val, PLACEHOLDERS["DOSSIER"]))
+        return RE_CSULT.sub(lambda _: f"N° : {PLACEHOLDERS['DOSSIER']}", line)
     m = RE_RPPS.search(line)
     if m:
         val = m.group(1); audit.append(PiiHit(page_idx, "RPPS", val, PLACEHOLDERS["RPPS"]))
@@ -975,6 +1013,7 @@ def _mask_line_by_regex(line: str, audit: List[PiiHit], page_idx: int, cfg: Dict
     def _repl_tel(m: re.Match) -> str:
         audit.append(PiiHit(page_idx, "TEL", m.group(0), PLACEHOLDERS["TEL"]))
         return PLACEHOLDERS["TEL"]
+    line = RE_TEL_SLASH.sub(_repl_tel, line)  # slash d'abord (plus spécifique)
     line = RE_TEL.sub(_repl_tel, line)
     line = RE_TEL_COMPACT.sub(_repl_tel, line)
 
@@ -1140,6 +1179,7 @@ def _mask_critical_in_key(key: str, audit: List[PiiHit], page_idx: int) -> str:
     def _repl_tel(m: re.Match) -> str:
         audit.append(PiiHit(page_idx, "TEL", m.group(0), PLACEHOLDERS["TEL"]))
         return PLACEHOLDERS["TEL"]
+    key = RE_TEL_SLASH.sub(_repl_tel, key)
     key = RE_TEL.sub(_repl_tel, key)
     key = RE_TEL_COMPACT.sub(_repl_tel, key)
     def _repl_email(m: re.Match) -> str:
@@ -1200,6 +1240,9 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
             return
         if tok.lower() in _FORCE_EXCLUDE:
             return
+        # Filtre supplémentaire : ne pas force-add les mots médicaux connus
+        if tok.lower() in _MEDICAL_STOP_WORDS_SET:
+            return
         names.add(tok)
         force_names.add(tok)
 
@@ -1324,10 +1367,10 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
 
     # --- Noms soignants sur la même ligne que "Note d'évolution" (ex: "Note d'évolution LACLAU-") ---
     for m in re.finditer(
-        r"Note\s+(?:IDE|AS|d'[ée]volution|m[ée]dicale|kin[ée])\s+"
+        r"Note[ \t]+(?:IDE|AS|d'[ée]volution|m[ée]dicale|kin[ée])[ \t]+"
-        r"(?:DR\.?\s+)?"
+        r"(?:DR\.?[ \t]+)?"
         r"([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]+)"
-        r"(?:\s+([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]+))?",
+        r"(?:[ \t]+([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]+))?",
         full_text
     ):
         for g in (m.group(1), m.group(2)):
@@ -1337,9 +1380,10 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
                     _add_name_force(tok)
 
     # --- "Signé" suivi directement d'un nom de soignant (ex: "Signé LARRIEU-") ---
+    # IMPORTANT: [ \t]+ (pas \s+) pour éviter de capturer les médicaments sur la ligne suivante
     for m in re.finditer(
-        r"Signé\s+(?!—|par\b)([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]+)"
+        r"Signé[ \t]+(?!—|par\b)([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]+)"
-        r"(?:\s+([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]+))?",
+        r"(?:[ \t]+([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]+))?",
         full_text
     ):
         for g in (m.group(1), m.group(2)):
@@ -1350,9 +1394,9 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
 
     # --- "Signé —" + médicament + nom soignant (ex: "Signé — PARACETAMOL BBM 1000 MG INJ NARZABAL") ---
     for m in re.finditer(
-        r"Signé\s+—\s+.*(?:INJ|COMP|GEL|PDR|SOL|PERF|SUSP|CAPS|CREM|SACHET|SIROP)\s+[-]?\s*"
+        r"Signé[ \t]+—[ \t]+.*(?:INJ|COMP|GEL|PDR|SOL|PERF|SUSP|CAPS|CREM|SACHET|SIROP)[ \t]+[-]?[ \t]*"
         r"([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]{2,})"
-        r"(?:\s+([A-ZÉÈÀÙÂÊÎÔÛ][a-zéèàùâêîôûäëïöüç]{2,}))?",
+        r"(?:[ \t]+([A-ZÉÈÀÙÂÊÎÔÛ][a-zéèàùâêîôûäëïöüç]{2,}))?",
         full_text
     ):
         for g in (m.group(1), m.group(2)):
@@ -1363,7 +1407,7 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
 
     # --- Noms soignants après conditionnement médicament (ex: "Flacon(s) LACROUTS") ---
     for m in re.finditer(
-        r"(?:Flacon|Ampoule|Seringue|Poche|Comprim[ée]|Gélule|Sachet)(?:\(s\))?\s+"
+        r"(?:Flacon|Ampoule|Seringue|Poche|Comprim[ée]|Gélule|Sachet)(?:\(s\))?[ \t]+"
         r"([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]{2,})",
         full_text
     ):
@@ -1373,8 +1417,8 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
 
     # --- "DR." / "DR" suivi d'un prénom seul (ex: "DR. Ute", "DR. Tam") dans les prescriptions ---
     for m in re.finditer(
-        r"DR\.?\s+([A-ZÉÈÀÙÂÊÎÔÛ][a-zéèàùâêîôûäëïöüç]{2,})"
+        r"DR\.?[ \t]+([A-ZÉÈÀÙÂÊÎÔÛ][a-zéèàùâêîôûäëïöüç]{2,})"
-        r"(?:\s+([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]+))?",
+        r"(?:[ \t]+([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\-]+))?",
         full_text
     ):
         for g in (m.group(1), m.group(2)):
@@ -1387,9 +1431,9 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
     # 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(
-        r"\d{1,2}\s*:\s*\d{2}\s+"
+        r"\d{1,2}[ \t]*:[ \t]*\d{2}[ \t]+"
         r"([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛ\-]{3,})"
-        r"(?:\s+([A-ZÉÈÀÙÂÊÎÔÛ][a-zéèàùâêîôûäëïöüç]{2,}))?",
+        r"(?:[ \t]+([A-ZÉÈÀÙÂÊÎÔÛ][a-zéèàùâêîôûäëïöüç]{2,}))?",
         full_text
     ):
         for g in (m.group(1), m.group(2)):
@@ -1415,13 +1459,15 @@ def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
     return filtered, hits, force_names
 
 
-def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> set:
+def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> Tuple[set, set]:
     """Pré-scan du document brut pour extraire les noms de personnes
     depuis les champs structurés (Patient, Rédigé par, etc.).
-    Retourne un ensemble de tokens (mots) à masquer globalement."""
+    Retourne (names, force_names) : ensemble de tokens à masquer,
+    et sous-ensemble qui bypass les stop words."""
     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()
 
     def _add_tokens(match_str: str):
         for token in match_str.split():
@@ -1434,6 +1480,17 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> set:
                 continue
             names.add(token)
 
+    def _add_tokens_force_all(match_str: str):
+        """Bypass stop words pour TOUS les tokens (contexte Patient: très fiable)."""
+        for token in match_str.split():
+            token = token.strip(" .-'")
+            if len(token) < 2:
+                continue
+            if token.upper() in wl_sections or token in wl_phrases:
+                continue
+            names.add(token)
+            force_names.add(token)
+
     def _add_tokens_force_first(match_str):
         """Comme _add_tokens mais force le 1er token (contexte Dr/Mme fort)."""
         tokens = match_str.split()
@@ -1441,21 +1498,20 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> set:
             token = token.strip(" .-'")
             if len(token) < 2:
                 continue
+            if token.upper() in wl_sections or token in wl_phrases:
+                continue
+            if token.lower() in _MEDICAL_STOP_WORDS_SET:
+                continue
             if i == 0:
-                # Premier token après Dr/Mme : toujours un nom, bypass stop words
+                # Premier token après Dr/Mme : contexte fiable
-                if token.upper() not in wl_sections:
+                names.add(token)
-                    names.add(token)
             else:
                 if len(token) < 3:
                     continue
-                if token.upper() in wl_sections or token in wl_phrases:
-                    continue
-                if token.lower() in _MEDICAL_STOP_WORDS_SET:
-                    continue
                 names.add(token)
 
     for m in RE_EXTRACT_PATIENT.finditer(full_text):
-        _add_tokens(m.group(1))
+        _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):
@@ -1482,6 +1538,9 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> set:
     # 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))
 
     # Extraction des noms dans les listes virgulées après Dr/Docteur
     # ex: "le Dr DUVAL, MACHELART, CHARLANNE, LAZARO, il a été proposé"
@@ -1509,7 +1568,7 @@ def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> set:
             if len(part) >= 3 and part.lower() not in _MEDICAL_STOP_WORDS_SET:
                 names.add(part)
 
-    return names
+    return names, force_names
 
 
 def _apply_extracted_names(text: str, names: set, audit: List[PiiHit], force_names: set = None) -> str:
@@ -1517,6 +1576,10 @@ def _apply_extracted_names(text: str, names: set, audit: List[PiiHit], force_nam
     placeholder = PLACEHOLDERS["NOM"]
     _force = force_names or set()
     safe_names = {n for n in names if len(n) >= 3 and (n in _force or n.lower() not in _MEDICAL_STOP_WORDS_SET)}
+    # Ajouter un hit global (page=-1) par nom pour la redaction PDF raster
+    # (un seul hit suffit — redact_pdf_raster cherche le token sur chaque page)
+    for token in sorted(safe_names, key=len, reverse=True):
+        audit.append(PiiHit(-1, "NOM_GLOBAL", token, placeholder))
     for token in sorted(safe_names, key=len, reverse=True):
         pattern = re.compile(rf"\b{re.escape(token)}\b", re.IGNORECASE)
         new_text = []
@@ -1577,7 +1640,7 @@ 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 = _extract_document_names(full_raw, cfg)
+    extracted_names, doc_force_names = _extract_document_names(full_raw, cfg)
 
     # Phase 0b : si document Trackare, extraction renforcée des PII structurés
     is_trackare = _is_trackare_document(full_raw)
@@ -1586,6 +1649,8 @@ def anonymise_document_regex(pages_text: List[str], tables_lines: List[List[str]
         trackare_names, trackare_hits, trackare_force_names = _extract_trackare_identity(full_raw)
         extracted_names.update(trackare_names)
         audit.extend(trackare_hits)
+    # Fusionner les force_names des deux sources
+    all_force_names = doc_force_names | trackare_force_names
 
     # Phase 0c : détection FINESS multiline (label et numéro sur lignes séparées,
     # avec possiblement 0-2 lignes intermédiaires masquées ou vides)
@@ -1595,6 +1660,32 @@ def anonymise_document_regex(pages_text: List[str], tables_lines: List[List[str]
     for m in _RE_FINESS_MULTILINE.finditer(full_raw):
         audit.append(PiiHit(-1, "FINESS", m.group(1), PLACEHOLDERS["FINESS"]))
 
+    # Phase 0d : date de naissance multiline (label et date sur lignes séparées)
+    # Ex: "Né(e) le :\n07/04/1943" ou "Date de naissance\n01/02/1950"
+    _RE_DATE_NAISSANCE_MULTILINE = re.compile(
+        r"(?:\bn[ée]+(?:\(?e?\)?)?\s+le|date\s+de\s+naissance|DDN)\s*[:\-]?\s*\n\s*"
+        r"(\d{1,2}[/.\-]\d{1,2}[/.\-]\d{2,4})",
+        re.IGNORECASE,
+    )
+    for m in _RE_DATE_NAISSANCE_MULTILINE.finditer(full_raw):
+        audit.append(PiiHit(-1, "DATE_NAISSANCE", m.group(1), PLACEHOLDERS["DATE_NAISSANCE"]))
+
+    # Phase 0e : IPP multiline (N°Ipp :\n20023294 ou I.P.P. :\nS1032021)
+    _RE_IPP_MULTILINE = re.compile(
+        r"(?:I\.?P\.?P\.?|IPP|N°\s*Ipp)\s*[:\-]?\s*\n\s*([A-Za-z0-9]{6,})\b",
+        re.IGNORECASE,
+    )
+    for m in _RE_IPP_MULTILINE.finditer(full_raw):
+        audit.append(PiiHit(-1, "IPP", m.group(1), PLACEHOLDERS["IPP"]))
+
+    # Phase 0f : DEMANDE N° multiline (DEMANDE N°\n2300261164)
+    _RE_DEMANDE_MULTILINE = re.compile(
+        r"DEMANDE\s+N[°o]?\s*\n\s*(\d{8,})",
+        re.IGNORECASE,
+    )
+    for m in _RE_DEMANDE_MULTILINE.finditer(full_raw):
+        audit.append(PiiHit(-1, "DOSSIER", m.group(1), PLACEHOLDERS["DOSSIER"]))
+
     # Phase 1 : masquage ligne par ligne (regex classiques)
     out_pages: List[str] = []
     for i, page_txt in enumerate(pages_text):
@@ -1620,7 +1711,7 @@ def anonymise_document_regex(pages_text: List[str], tables_lines: List[List[str]
 
     # Phase 2 : application globale des noms extraits (rattrapage)
     if extracted_names:
-        text_out = _apply_extracted_names(text_out, extracted_names, audit, force_names=trackare_force_names)
+        text_out = _apply_extracted_names(text_out, extracted_names, audit, force_names=all_force_names)
 
     # Phase 2b : application globale des PiiHit (EPISODE, RPPS, FINESS)
     text_out = _apply_trackare_hits_to_text(text_out, audit)
@@ -1806,6 +1897,7 @@ def selective_rescan(text: str, cfg: Dict[str, Any] | None = None) -> str:
     protected, kept = strip_tables(text)
     # PII critiques (comme avant)
     protected = RE_EMAIL.sub(PLACEHOLDERS["EMAIL"], protected)
+    protected = RE_TEL_SLASH.sub(PLACEHOLDERS["TEL"], protected)
     protected = RE_TEL.sub(PLACEHOLDERS["TEL"], protected)
     protected = RE_TEL_COMPACT.sub(PLACEHOLDERS["TEL"], protected)
     protected = RE_IBAN.sub(PLACEHOLDERS["IBAN"], protected)
@@ -1846,6 +1938,10 @@ def selective_rescan(text: str, cfg: Dict[str, Any] | None = None) -> str:
         tokens = [t for t in span.split() if t]
         if len(tokens) == 1 and len(tokens[0]) <= 3:
             return raw
+        # Filtrer les termes médicaux (stop words)
+        clean = [t for t in tokens if t.lower() not in _MEDICAL_STOP_WORDS_SET]
+        if not clean:
+            return raw
         return raw.replace(span, PLACEHOLDERS["NOM"])
     protected = RE_PERSON_CONTEXT.sub(_rescan_person, protected)
     res = list(protected)
@@ -1971,7 +2067,7 @@ def redact_pdf_vector(original_pdf: Path, audit: List[PiiHit], out_pdf: Path, oc
                 compact = re.sub(r"\s+", "", token)
                 if compact != token:
                     rects = page.search_for(compact)
-            if not rects and " " in token and h.kind in {"NOM", "NOM_EXTRACTED", "NER_PER", "EDS_NOM"}:
+            if not rects and " " in token and h.kind in {"NOM", "NOM_GLOBAL", "NOM_EXTRACTED", "NER_PER", "EDS_NOM"}:
                 for word in token.split():
                     word = word.strip(" .-'")
                     if len(word) < 5 or word.lower() in _MEDICAL_STOP_WORDS_SET:
@@ -2074,7 +2170,7 @@ def redact_pdf_raster(original_pdf: Path, audit: List[PiiHit], out_pdf: Path, dp
             if not found and h.kind in {"NIR", "IBAN", "TEL", "VLM_TEL", "VLM_NIR"}:
                 compact = re.sub(r"\s+", "", token)
                 found = page.search_for(compact)
-            if not found and " " in token and h.kind in {"NOM", "NOM_EXTRACTED", "NER_PER", "EDS_NOM",
+            if not found and " " in token and h.kind in {"NOM", "NOM_GLOBAL", "NOM_EXTRACTED", "NER_PER", "EDS_NOM",
                                                           "VLM_NOM", "VLM_ETAB", "VLM_SERVICE"}:
                 for word in token.split():
                     word = word.strip(" .-'")
@@ -2359,13 +2455,13 @@ def process_pdf(
     # 4b) Propagation globale SÉLECTIVE : uniquement pour les PII critiques
     # Les PII critiques (DATE_NAISSANCE, NIR, IPP, EMAIL) sont propagés sur toutes les pages
     # pour éviter les fuites sur les documents multi-pages (ex: CRO)
-    _CRITICAL_PII_TYPES = {"DATE_NAISSANCE", "NIR", "IPP", "EMAIL", "force_term", "force_regex", "FINESS"}
+    _CRITICAL_PII_TYPES = {"DATE_NAISSANCE", "NIR", "IPP", "EMAIL", "force_term", "force_regex", "FINESS", "DOSSIER"}
-    
+
     _global_pii: Dict[str, set] = {}
     for h in anon.audit:
         # Collecter TOUS les types pour analyse, mais ne propager que les critiques
         if h.kind in {"TEL", "EMAIL", "ADRESSE", "CODE_POSTAL", "EPISODE", "RPPS", "VILLE", "ETAB",
-                      "VLM_SERVICE", "VLM_ETAB", "DATE_NAISSANCE", "NIR", "IPP",
+                      "VLM_SERVICE", "VLM_ETAB", "DATE_NAISSANCE", "NIR", "IPP", "DOSSIER",
                       "force_term", "force_regex", "FINESS"}:
             # Traitement spécial pour DATE_NAISSANCE : extraire la date pure et générer toutes les variations
             if h.kind == "DATE_NAISSANCE":

config/dictionnaires.yml

@@ -18,11 +18,15 @@ blacklist:
   force_mask_terms:
   - CENTRE HOSPITALIER COTE BASQUE
   - CENTRE HOSPITALIER DE LA COTE BASQUE
+  - POLYCLINIQUE COTE BASQUE SUD
+  - POLYCLINIQUE CÔTE BASQUE SUD
   - CHCB
+  - '640780417'
   - 'Dates du séjour :'
   - CONCERTATION
   force_mask_regex:
   - 'Centre\s+Hospitalier\s+(?:de\s+(?:la\s+)?)?C[oôÔ]te\s+Basque'
+  - 'Polyclinique\s+C[oôÔ]te\s+Basque\s+Sud'
 kv_labels_preserve:
 - FINESS
 - IPP

run_batch_30_audit.py

@@ -10,42 +10,43 @@ sys.path.insert(0, str(Path(__file__).parent))
 
 import anonymizer_core_refactored_onnx as core
 from eds_pseudo_manager import EdsPseudoManager
+from vlm_manager import VlmManager
 
 SRC = Path("/home/dom/Téléchargements/II-1 Ctrl_T2A_2025_CHCB_DocJustificatifs (1)")
 OUTDIR = SRC / "anonymise_audit_30"
 CONFIG = Path("/home/dom/ai/anonymisation/config/dictionnaires.yml")
 
 PDFS = [
-    SRC / "110_23061319/trackare-07026002-23061319_07026002_23061319.pdf",
+    SRC / "114_23060661/CONSULTATION ANESTHESISTE 23060661.pdf",
-    SRC / "115_23066188/CRH 23066188.pdf",
+    SRC / "124_23074376/trackare-05000272-23074376_05000272_23074376.pdf",
-    SRC / "161_23098838/CRO 23098838.pdf",
+    SRC / "133_23056022/CONSULTATION ANESTHESISTE 23056022.pdf",
-    SRC / "179_23126805/trackare-23005591-23126805_23005591_23126805.pdf",
+    SRC / "141_23090597/trackare-BA042686-23090597_BA042686_23090597.pdf",
-    SRC / "181_23127286/CRH 23127286.pdf",
+    SRC / "148_23018396/trackare-23000862-23018396_23000862_23018396.pdf",
-    SRC / "192_23132490/CRH 23132490.pdf",
+    SRC / "183_23087212/LETTRE DE SORTIE 23087212.pdf",
-    SRC / "208_23151988/trackare-23020064-23151988_23020064_23151988.pdf",
+    SRC / "216_23159905/CRO 23159905.pdf",
-    SRC / "215_23158603/trackare-22028007-23158603_22028007_23158603.pdf",
+    SRC / "216_23159905/trackare-99246761-23159905_99246761_23159905.pdf",
-    SRC / "227_23173599/CRH 23173599.pdf",
+    SRC / "222_23139653/CONSULTATION ANESTHESISTE 23139653.pdf",
-    SRC / "236_23116794/trackare-BA054633-23116794_BA054633_23116794.pdf",
+    SRC / "225_23160703/CRO 23160703.pdf",
-    SRC / "248_23194278/CRH 23194278.pdf",
+    SRC / "26_23127395/trackare-BA192486-23127395_BA192486_23127395.pdf",
-    SRC / "263_23203642/CRO 23203642.pdf",
+    SRC / "269_23232115/BACTERIO 23232115.pdf",
-    SRC / "28_23135549/trackare-15021750-23135549_15021750_23135549.pdf",
+    SRC / "290_23025988/CR consultation anesth-290-23025988.pdf",
-    SRC / "321_23043929/CRH 321_23066387.pdf",
+    SRC / "315_23060770/trackare-05012965-23060770_05012965_23060770.pdf",
-    SRC / "379_23098754/trackare-18009635-23098754_18009635_23098754.pdf",
+    SRC / "385_23102874/trackare-BA065989-23102874_BA065989_23102874.pdf",
-    SRC / "39_23167029/trackare-23022121-23167029_23022121_23167029.pdf",
+    SRC / "433_23135726/trackare-BA127127-23135726_BA127127_23135726.pdf",
-    SRC / "444_23141032/trackare-BA102259-23141032_BA102259_23141032.pdf",
-    SRC / "478_23161697/cro 478_23161697.pdf",
-    SRC / "50_23219173/trackare-07019278-23219173_07019278_23219173.pdf",
     SRC / "520_23177582/trackare-99252128-23177582_99252128_23177582.pdf",
-    SRC / "556_23220878/trackare-21041742-23220878_21041742_23220878.pdf",
+    SRC / "552_23214501/trackare-BA171849-23214501_BA171849_23214501.pdf",
-    SRC / "602_23070052/trackare-20028293-23070052_20028293_23070052.pdf",
+    SRC / "590_23043950/trackare-17015185-23043950_17015185_23043950.pdf",
-    SRC / "604_23070704/trackare-23008170-23070704_23008170_23070704.pdf",
+    SRC / "60_23106634/CRH 60_23106634.pdf",
-    SRC / "655_23163458/trackare-01296746-23163458_01296746_23163458.pdf",
+    SRC / "603_23070213/trackare-00260974-23070213_00260974_23070213.pdf",
-    SRC / "684_23207941/CRH 684_23207941.pdf",
+    SRC / "609_23076655/trackare-BA067657-23076655_BA067657_23076655.pdf",
-    SRC / "79_23187785/79_23187785 Dossier.pdf",
+    SRC / "625_23098722/trackare-05012679-23098722_05012679_23098722.pdf",
-    SRC / "12_23084754/CRO 23084754.pdf" if (SRC / "12_23084754/CRO 23084754.pdf").exists() else SRC / "122_23070126/LETTRE DE SORTIE 23070126.pdf",
+    SRC / "632_23124019/trackare-11004431-23124019_11004431_23124019.pdf",
-    SRC / "122_23070126/LETTRE DE SORTIE 23070126.pdf",
+    SRC / "639_23135847/trackare-07003136-23135847_07003136_23135847.pdf",
-    SRC / "131_23079402/CRH 23079402.pdf",
+    SRC / "656_23165708/trackare-13013848-23165708_13013848_23165708.pdf",
-    SRC / "290_23025988/cr anesth 290_23025988.pdf",
+    SRC / "664_23175616/trackare-03020576-23175616_03020576_23175616.pdf",
+    SRC / "8_23074520/trackare-BA093659-23074520_BA093659_23074520.pdf",
+    SRC / "88_23034958/trackare-14025311-23034958_14025311_23034958.pdf",
+    SRC / "89_23016863/trackare-BA121804-23016863_BA121804_23016863.pdf",
 ]
 
 
@@ -54,7 +55,16 @@ def main():
     ner = EdsPseudoManager()
     ner.load()
     assert ner.is_loaded(), "EDS-Pseudo non chargé"
-    print("EDS-Pseudo chargé.\n", flush=True)
+    print("EDS-Pseudo chargé.", flush=True)
+
+    print("Chargement VLM (Ollama qwen2.5vl:7b)...", flush=True)
+    vlm = VlmManager()
+    try:
+        vlm.load()
+        print(f"VLM chargé.\n", flush=True)
+    except Exception as e:
+        print(f"VLM indisponible ({e}), on continue sans.\n", flush=True)
+        vlm = None
 
     # Vérifier existence des fichiers
     existing = [p for p in PDFS if p.exists()]
@@ -86,6 +96,7 @@ def main():
                 ner_manager=ner,
                 ner_thresholds=None,
                 ogc_label=ogc,
+                vlm_manager=vlm,
             )
             audit_path = Path(outputs.get("audit", ""))
             if audit_path.exists():