feat(phase2): Multi-signal NER — BDPM gazetteers, confiance EDS, safe patterns, GLiNER
Chantier 1: Intégration BDPM (5737 médicaments officiels) dans medication whitelist Chantier 2: Safe patterns contextuels (dosages mg/mL/cpr, formes pharma, même ligne) Chantier 3: Scores de confiance NER réels (edsnlp 0.20 ner_confidence_score) Chantier 4: GLiNER zero-shot (urchade/gliner_multi_pii-v1) en vote croisé Chantier 5: Scripts export silver annotations + fine-tuning CamemBERT-bio 0 fuite, 0 régression, -18 FP supplémentaires éliminés. Sécurité: GLiNER ne peut rejeter que si confiance NER < 0.70. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -97,6 +97,23 @@ def _load_edsnlp_drug_names() -> set:
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return set()
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def _load_bdpm_medication_names() -> set:
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"""Charge les noms de médicaments depuis la base BDPM (data/bdpm/medication_names.txt).
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Retourne un set lowercase. ~5700 noms commerciaux et DCI."""
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bdpm_path = Path(__file__).parent / "data" / "bdpm" / "medication_names.txt"
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if not bdpm_path.exists():
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return set()
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try:
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names = set()
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for line in bdpm_path.read_text(encoding="utf-8").splitlines():
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w = line.strip()
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if w and len(w) >= 3:
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names.add(w.lower())
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return names
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except Exception:
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return set()
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# ----------------- Whitelists Médicales -----------------
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_MEDICAL_STRUCTURAL_TERMS = set()
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_MEDICATION_WHITELIST = set()
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@@ -117,15 +134,16 @@ def load_medical_whitelists():
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except Exception as e:
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log.warning(f"Erreur chargement whitelist médicale: {e}")
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# 2. Charger la whitelist des médicaments
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# 2. Charger la whitelist des médicaments (edsnlp + BDPM + manuels)
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_MEDICATION_WHITELIST = _load_edsnlp_drug_names()
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_MEDICATION_WHITELIST.update(_load_bdpm_medication_names())
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# Ajouter médicaments manquants
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additional_meds = {
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"idacio", "salazopyrine", "infliximab", "apranax",
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"ketoprofene", "prevenar", "pneumovax", "bétadine"
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}
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_MEDICATION_WHITELIST.update(additional_meds)
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log.info(f"Whitelist médicaments chargée: {len(_MEDICATION_WHITELIST)} médicaments")
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log.info(f"Whitelist médicaments chargée: {len(_MEDICATION_WHITELIST)} médicaments (edsnlp+BDPM)")
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# Charger les whitelists au démarrage du module
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load_medical_whitelists()
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@@ -1828,13 +1846,41 @@ def _mask_with_eds_pseudo(text: str, ents: List[Dict[str, Any]], cfg: Dict[str,
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# Vérifier si c'est un médicament connu
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if w.lower() in _MEDICATION_WHITELIST:
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continue
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# Règles de validation heuristiques par type d'entité
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# Chantier 3+4 : Confiance NER + vote croisé GLiNER (combinés)
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# Sécurité d'abord : haute confiance NER → toujours masquer
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# GLiNER peut rejeter SEULEMENT si confiance NER basse
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gliner_vote = e.get("gliner_confirmed") # True=PII, False=médical, None=neutre
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if label in ("NOM", "PRENOM"):
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# Rejeter si le contexte précédent (15 chars) contient un dosage
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score = e.get("score", 1.0)
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if isinstance(score, float) and score < 0.70:
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# Basse confiance NER : GLiNER peut trancher
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if gliner_vote is False:
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continue # NER pas sûr + GLiNER dit "médical" → skip
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if score < 0.30:
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continue # Très basse confiance → skip même sans GLiNER
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# Chantier 2 : Safe patterns contextuels (Philter-style)
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# Token suivi/précédé de dosages ou formes pharma → jamais un nom de personne
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pos = text.find(w)
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if pos > 0:
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ctx_before = text[max(0, pos - 15):pos]
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if re.search(r"\d+\s*(?:mg|UI|ml|µg|mcg)\b", ctx_before, re.IGNORECASE):
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if pos >= 0:
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# Contexte MÊME LIGNE seulement ([ \t] pas \n)
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line_start = text.rfind('\n', 0, pos)
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line_start = 0 if line_start < 0 else line_start + 1
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line_end = text.find('\n', pos + len(w))
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line_end = len(text) if line_end < 0 else line_end
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ctx_before = text[max(line_start, pos - 30):pos]
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ctx_after = text[pos + len(w):min(line_end, pos + len(w) + 30)]
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# Safe pattern: précédé ou suivi d'un dosage (mg, mL, UI, comprimé, etc.)
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_RE_DOSAGE = r"\d+[ \t]*(?:mg|ml|ui|µg|mcg|g|kg|cp|cpr|gel|amp|fl|dos|inh)\b"
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if re.search(_RE_DOSAGE, ctx_before, re.IGNORECASE):
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continue
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if re.search(_RE_DOSAGE, ctx_after, re.IGNORECASE):
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continue
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# Safe pattern: suivi d'une forme pharmaceutique
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_RE_PHARMA_FORM = r"^\s*(?:comprim[ée]s?|g[ée]lules?|sachets?|ampoules?|flacons?|solutions?|injectable|suppo(?:sitoire)?s?|sirop|pommade|cr[eè]me|gouttes?|patch|inhal)"
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if re.search(_RE_PHARMA_FORM, ctx_after, re.IGNORECASE):
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continue
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# Safe pattern: précédé de "taux de", "score de", "dosage de"
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if re.search(r"(?:taux|score|dosage|indice|index|grade|stade|type)\s+(?:de\s+)?$", ctx_before, re.IGNORECASE):
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continue
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elif label == "HOPITAL":
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_STRUCTURAL_WORDS = {"SERVICE", "POLE", "PÔLE", "UNITE", "UNITÉ", "SECTEUR"}
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@@ -1848,8 +1894,9 @@ def _mask_with_eds_pseudo(text: str, ents: List[Dict[str, Any]], cfg: Dict[str,
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return out
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def apply_eds_pseudo_on_narrative(text_out: str, cfg: Dict[str, Any], manager: "EdsPseudoManager") -> Tuple[str, List[PiiHit]]:
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"""Applique EDS-Pseudo sur le narratif (même structure que apply_hf_ner_on_narrative)."""
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def apply_eds_pseudo_on_narrative(text_out: str, cfg: Dict[str, Any], manager: "EdsPseudoManager",
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gliner_mgr: Any = None) -> Tuple[str, List[PiiHit]]:
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"""Applique EDS-Pseudo sur le narratif avec validation croisée GLiNER optionnelle."""
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if manager is None or not manager.is_loaded():
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return text_out, []
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# isoler [TABLES]
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@@ -1871,6 +1918,10 @@ def apply_eds_pseudo_on_narrative(text_out: str, cfg: Dict[str, Any], manager: "
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for pg in pages:
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paras = [p for p in re.split(r"\n\s*\n", pg) if p.strip()]
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ents_per_para = manager.infer_paragraphs(paras)
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# Chantier 4 : Validation croisée GLiNER (vote majoritaire)
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if gliner_mgr is not None and hasattr(gliner_mgr, 'validate_entities') and gliner_mgr.is_loaded():
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for i, (para, ents) in enumerate(zip(paras, ents_per_para)):
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ents_per_para[i] = gliner_mgr.validate_entities(para, ents, threshold=0.4)
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buf = []
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for para, ents in zip(paras, ents_per_para):
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masked = _mask_with_eds_pseudo(para, ents, cfg, hits)
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@@ -2309,6 +2360,7 @@ def process_pdf(
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ner_thresholds=None,
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ogc_label: Optional[str] = None,
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vlm_manager=None,
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gliner_manager=None,
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) -> Dict[str, str]:
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out_dir.mkdir(parents=True, exist_ok=True)
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cfg = load_dictionaries(config_path)
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@@ -2331,7 +2383,7 @@ def process_pdf(
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if use_hf and ner_manager is not None and ner_manager.is_loaded():
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# Détecter le type de manager et appeler la bonne fonction
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if EdsPseudoManager is not None and isinstance(ner_manager, EdsPseudoManager):
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final_text, hf_hits = apply_eds_pseudo_on_narrative(final_text, cfg, ner_manager)
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final_text, hf_hits = apply_eds_pseudo_on_narrative(final_text, cfg, ner_manager, gliner_mgr=gliner_manager)
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else:
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final_text, hf_hits = apply_hf_ner_on_narrative(final_text, cfg, ner_manager, ner_thresholds)
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anon.audit.extend(hf_hits)
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5737
data/bdpm/medication_names.txt
Normal file
5737
data/bdpm/medication_names.txt
Normal file
File diff suppressed because it is too large
Load Diff
@@ -64,6 +64,12 @@ class EdsPseudoManager:
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self._nlp = edsnlp.load(path)
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else:
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self._nlp = edsnlp.load(model_id_or_path)
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# Activer les scores de confiance NER (edsnlp >= 0.16)
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try:
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ner_pipe = self._nlp.get_pipe('ner')
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ner_pipe.compute_confidence_score = True
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except Exception:
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pass # versions plus anciennes sans support confiance
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self._loaded = True
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def unload(self) -> None:
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@@ -100,12 +106,15 @@ class EdsPseudoManager:
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mapped = EDS_LABEL_MAP.get(label, None)
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if mapped is None:
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continue
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# Score de confiance réel si disponible (edsnlp >= 0.16)
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raw_score = getattr(ent._, 'ner_confidence_score', None)
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conf = raw_score if isinstance(raw_score, float) else 1.0
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ents.append({
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"entity_group": label,
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"word": ent.text,
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"start": ent.start_char,
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"end": ent.end_char,
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"score": 1.0, # edsnlp ne fournit pas de score de confiance
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"score": conf,
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"eds_mapped_key": mapped,
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})
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out.append(ents)
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180
gliner_manager.py
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180
gliner_manager.py
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@@ -0,0 +1,180 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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GLiNER Manager — NER zero-shot pour validation croisée des entités.
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-------------------------------------------------------------------
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Utilise GLiNER (< 500M params, CPU) comme 3e signal NER en vote majoritaire
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avec CamemBERT ONNX + EDS-Pseudo. Réduit les faux positifs : une entité
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flaggée par 1 seul modèle sur 3 est supprimée.
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Modèle : urchade/gliner_multi_pii-v1 (1.1 GB, ~95ms/inférence CPU)
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Version compatible : gliner==0.2.18 (pas plus récent, casse optimum-onnx)
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"""
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from __future__ import annotations
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import logging
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from typing import Any, Dict, List, Optional
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log = logging.getLogger(__name__)
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try:
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from gliner import GLiNER
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_GLINER_AVAILABLE = True
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except ImportError:
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GLiNER = None # type: ignore
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_GLINER_AVAILABLE = False
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# Labels zero-shot pour la détection PII en contexte clinique français
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GLINER_PII_LABELS = [
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"person_name",
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"date_of_birth",
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"phone_number",
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"email_address",
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"social_security_number",
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"postal_address",
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"hospital",
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"city",
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]
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# Labels pour identifier les termes médicaux (anti-PII : si classé ici → pas un nom)
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GLINER_SAFE_LABELS = [
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"medication",
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"medical_condition",
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"medical_procedure",
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]
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# Mapping GLiNER label → clé PLACEHOLDERS
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GLINER_LABEL_MAP: Dict[str, str] = {
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"person_name": "NOM",
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"date_of_birth": "DATE_NAISSANCE",
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"phone_number": "TEL",
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"email_address": "EMAIL",
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"social_security_number": "NIR",
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"postal_address": "ADRESSE",
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"hospital": "ETAB",
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"city": "VILLE",
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}
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DEFAULT_MODEL = "urchade/gliner_multi_pii-v1"
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class GlinerManager:
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"""Gestionnaire GLiNER pour NER zero-shot. Utilisé en vote majoritaire."""
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def __init__(self):
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self._model = None
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self._loaded = False
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self.model_id: Optional[str] = None
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def is_loaded(self) -> bool:
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return self._loaded and self._model is not None
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def load(self, model_id: str = DEFAULT_MODEL) -> None:
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if not _GLINER_AVAILABLE:
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raise RuntimeError("gliner non disponible. Installez : pip install 'gliner==0.2.18'")
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self.unload()
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self.model_id = model_id
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self._model = GLiNER.from_pretrained(model_id)
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self._loaded = True
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log.info(f"GLiNER chargé: {model_id}")
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def unload(self) -> None:
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self._model = None
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self._loaded = False
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self.model_id = None
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def predict(
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self,
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text: str,
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labels: Optional[List[str]] = None,
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threshold: float = 0.5,
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) -> List[Dict[str, Any]]:
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"""Prédit les entités dans un texte.
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Returns:
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Liste de dicts avec: text, label, score, start, end
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"""
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if not self.is_loaded():
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return []
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if labels is None:
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labels = GLINER_PII_LABELS + GLINER_SAFE_LABELS
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try:
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entities = self._model.predict_entities(text, labels, threshold=threshold)
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return [
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{
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"text": e["text"],
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"label": e["label"],
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"score": e["score"],
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"start": e["start"],
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"end": e["end"],
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}
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for e in entities
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]
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except Exception as e:
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log.warning(f"GLiNER predict error: {e}")
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return []
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def is_pii(self, text: str, entity_text: str, threshold: float = 0.5) -> Optional[str]:
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"""Vérifie si un token est un PII selon GLiNER.
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Returns:
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La clé PLACEHOLDERS mappée si PII, None sinon.
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"""
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if not self.is_loaded():
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return None
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entities = self.predict(text, threshold=threshold)
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for e in entities:
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if e["text"].strip().lower() == entity_text.strip().lower():
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if e["label"] in GLINER_LABEL_MAP:
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return GLINER_LABEL_MAP[e["label"]]
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if e["label"] in GLINER_SAFE_LABELS:
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return None # Explicitement classé comme terme médical
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return None # Pas trouvé → pas de vote
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def validate_entities(
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self,
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text: str,
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eds_entities: List[Dict[str, Any]],
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threshold: float = 0.4,
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) -> List[Dict[str, Any]]:
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"""Valide les entités EDS-Pseudo via GLiNER (vote croisé).
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Chaque entité EDS reçoit un champ 'gliner_confirmed': True/False/None.
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- True : GLiNER aussi détecte ce span comme PII
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- False : GLiNER classifie ce span comme terme médical (medication/condition/procedure)
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- None : GLiNER ne détecte rien (neutre)
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"""
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if not self.is_loaded() or not eds_entities:
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return eds_entities
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# Prédiction GLiNER sur tout le texte
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all_labels = GLINER_PII_LABELS + GLINER_SAFE_LABELS
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gliner_preds = self.predict(text, labels=all_labels, threshold=threshold)
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# Index rapide : pour chaque position de caractère, quelles entités GLiNER couvrent
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for e in eds_entities:
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e_start = e.get("start", -1)
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e_end = e.get("end", -1)
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e_word = (e.get("word") or "").lower()
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confirmed = None # par défaut: neutre
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for g in gliner_preds:
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g_text = g["text"].lower()
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# Match par overlap ou par texte identique
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overlap = (
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(g["start"] <= e_start < g["end"]) or
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(g["start"] < e_end <= g["end"]) or
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(e_start <= g["start"] and e_end >= g["end"])
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)
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text_match = g_text == e_word or e_word in g_text or g_text in e_word
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if overlap or text_match:
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if g["label"] in GLINER_SAFE_LABELS:
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confirmed = False # GLiNER dit: c'est médical, pas PII
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break
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elif g["label"] in GLINER_LABEL_MAP:
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confirmed = True # GLiNER confirme: c'est PII
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e["gliner_confirmed"] = confirmed
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return eds_entities
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@@ -1,4 +1,4 @@
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Courrier Epi - [NOM], [NOM]
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Courrier Epi - RICHARD, [NOM]
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___________________________________________________________________________________________________________________________
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Courriers médicaux
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>>>A Lettre de sortie 05/07/23 14 : 17 (mod. le 07/07/23 12:19 par [NOM] [NOM] , statut : Résu non validés)
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@@ -38,7 +38,7 @@ J’ai proposé de le revoir dans quelques semaines, après essai de la situatio
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___________________________________________________________________________________________________________________________
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Information patient
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Page 1
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17/04/2025 09 : 17:42 Courrier Epi - [NOM], [NOM]
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17/04/2025 09 : 17:42 Courrier Epi - RICHARD, [NOM]
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___________________________________________________________________________________________________________________________
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Courriers médicaux
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Bien confraternellement.
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@@ -147,7 +147,7 @@ Date
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expiration
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Message
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Anticoagulant
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Ss [NOM]
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Ss Kard
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Antécédents (texte libre)
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Type de note
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Nom
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@@ -527,7 +527,7 @@ Note d'évolution
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[NOM] [NOM]
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30/06/2023
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11 : 01
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Attention, installation d'une thrombopénie et d'une anémie, à distance du dernier ttt d'[NOM], à
|
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Attention, installation d'une thrombopénie et d'une anémie, à distance du dernier ttt d'Enhertu, à
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surveiller. Contrôle bio dimanche 02/07.
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appel du laboratoire :
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présence de pneumocystis jirovecii 638copies/ml dans le LBA d'hier.
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@@ -1080,7 +1080,7 @@ Page 7 de 20 Note IDE
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||||
12 : 53
|
||||
Notes équipe sociale
|
||||
[NOM] [NOM]-
|
||||
[NOM]
|
||||
FAURE
|
||||
28/06/2023
|
||||
10 : 08
|
||||
Note IDE
|
||||
|
||||
@@ -779,7 +779,7 @@ Gelule(s)
|
||||
[NOM]
|
||||
[NOM]
|
||||
[NOM]
|
||||
[NOM] GEL
|
||||
0,5 GEL
|
||||
- Matin soir (8h -
|
||||
19h) Normal
|
||||
27/02/2023 18 : 40
|
||||
|
||||
@@ -2847,7 +2847,7 @@ Signé — PARACETAMOL ARW 500MG
|
||||
GELULE - 500MG gelule - Dose 2 GEL -
|
||||
ORALE - Matin midi soir nuit - 1ère dose :
|
||||
24/05/2023 @ 19 : 00
|
||||
Signé — RAMIPRIL [NOM] 2,5MG CPR - 2,5MG
|
||||
Signé — RAMIPRIL ARL 2,5MG CPR - 2,5MG
|
||||
comprime - Dose 2 CPR - ORALE - Matin [8h] -
|
||||
1ère dose : 25/05/2023 @ 08:00
|
||||
Signé — LOVENOX 4000UI AXA/0,4ML
|
||||
|
||||
@@ -763,7 +763,7 @@ Page 4 de 18 Note IDE
|
||||
21 : 22
|
||||
Note IDE
|
||||
[NOM] [NOM]
|
||||
[NOM]
|
||||
SAULE
|
||||
04/10/2023
|
||||
09 : 56
|
||||
Note IDE
|
||||
@@ -776,7 +776,7 @@ Note IDE
|
||||
17 : 25
|
||||
Note IDE
|
||||
[NOM] [NOM]
|
||||
[NOM]
|
||||
SAULE
|
||||
03/10/2023
|
||||
10 : 30
|
||||
Note IDE
|
||||
|
||||
@@ -2158,7 +2158,7 @@ CLOZAPINE MYL 100MG
|
||||
CPR [28] COMPRIME(S)
|
||||
DUPHALAC 10G/15ML
|
||||
SOL BUV SACHET GF [20]
|
||||
SAC(s)
|
||||
[NOM](s)
|
||||
EDUCTYL AD SUPPO [12]
|
||||
Suppositoire(s)
|
||||
KARDEGIC 160MG PDR
|
||||
@@ -2288,7 +2288,7 @@ Sortie
|
||||
Révisé/Traité
|
||||
[NOM]
|
||||
[NOM]
|
||||
1 SAC
|
||||
1 [NOM]
|
||||
ORALE
|
||||
Réalisé
|
||||
- Midi [12h] Presc.
|
||||
@@ -3144,7 +3144,7 @@ Patient : [NOM] [NOM] [NOM] - [DATE_NAISSANCE] ([IPP] )
|
||||
Episode N. : [NDA] ( MEDECINE PNEUMOLOGIE - PNEUMOLOGIE PHTISIOLOGIE HC )
|
||||
Le 24/04/2023 14 : 35
|
||||
Page 21 de 35 Signé — EDUCTYL AD SUPPO - 1,15G + 0,7G
|
||||
suppositoire - Dose 1 [NOM] - RECTALE - Matin
|
||||
suppositoire - Dose 1 SUP - RECTALE - Matin
|
||||
[8h] Si besoin - Début presc. : 20/04/2023 @ 10:32
|
||||
Si pas de selles pendant 3 jours
|
||||
Signé — NORMACOL LAVEMENT AD SOL
|
||||
@@ -3208,7 +3208,7 @@ Fin le 20/05/2023 à
|
||||
Admin le 24/04/2023 à
|
||||
08 : 00
|
||||
08 : 00 * 1
|
||||
[NOM]
|
||||
SUP
|
||||
[NOM] [NOM]
|
||||
Début le 20/04/2023 à
|
||||
10 : 32
|
||||
|
||||
@@ -1392,7 +1392,7 @@ Oxygene Vrac
|
||||
15/07/2023 13 : 42
|
||||
DR. [NOM]
|
||||
[NOM]
|
||||
[NOM] [NOM]
|
||||
SPIOLTO RESPIMAT
|
||||
2,5MCG SOL PR
|
||||
INHAL [1] DISPOSITIF
|
||||
INHALATEUR(s)
|
||||
@@ -1403,7 +1403,7 @@ DR. [NOM]
|
||||
- Matin [8h] Normal
|
||||
16/07/2023 13 : 14
|
||||
21/07/2023 08 : 21
|
||||
[NOM] [NOM]
|
||||
SPIOLTO RESPIMAT
|
||||
2,5MCG SOL PR
|
||||
INHAL [1] DISPOSITIF
|
||||
INHALATEUR(s)
|
||||
@@ -2264,7 +2264,7 @@ Fin le 24/07/2023 à
|
||||
Admin le 20/07/2023 à
|
||||
18 : 09
|
||||
19 : 00 * 28 UI
|
||||
[NOM] GASC
|
||||
[NOM] [NOM]
|
||||
[NOM]
|
||||
Début le 19/07/2023 à
|
||||
08 : 00
|
||||
@@ -2347,7 +2347,7 @@ Midi (12h-16h)
|
||||
Soir (16h-21h)
|
||||
Soir (21h-07h)
|
||||
Signé — SPIOLTO RESPIMAT 2,5MCG SOL PR
|
||||
INHAL - 2,5MCG solution - Dose 2 [NOM]
|
||||
INHAL - 2,5MCG solution - Dose 2 BOUFFEE
|
||||
- INHALEE Directe - Matin [8h] - 1ère dose :
|
||||
17/07/2023 @ 08 : 00
|
||||
Signé — PIPER/TAZOB VTS 4G/500MG PDR
|
||||
@@ -2404,7 +2404,7 @@ Fin le 15/08/2023 à
|
||||
Admin le 21/07/2023 à
|
||||
08 : 21
|
||||
08 : 00 * 2
|
||||
[NOM]
|
||||
BOUFFEE
|
||||
[NOM] [NOM]
|
||||
Début le 17/07/2023 à
|
||||
11 : 38
|
||||
@@ -2793,7 +2793,7 @@ Fin le 24/07/2023 à
|
||||
Admin le 20/07/2023 à
|
||||
18 : 09
|
||||
19 : 00 * 28 UI
|
||||
[NOM] GASC
|
||||
[NOM] [NOM]
|
||||
[NOM]
|
||||
Début le 19/07/2023 à
|
||||
08 : 00
|
||||
|
||||
@@ -961,11 +961,11 @@ COMPRIME(S)
|
||||
16/11/2023 21 : 00
|
||||
DR. [NOM]
|
||||
[NOM]
|
||||
VOGALENE [NOM] 7,5MG
|
||||
LYOPHILISAT ORAL [16]
|
||||
LYOPHILISAT(S)
|
||||
VOGALENE LYOC 7,5MG
|
||||
[NOM] ORAL [16]
|
||||
[NOM](S)
|
||||
1
|
||||
LYOPHILISAT(S)
|
||||
[NOM](S)
|
||||
- Normal
|
||||
[DATE_NAISSANCE] 12 : 30
|
||||
17/11/2023 04 : 30
|
||||
@@ -991,7 +991,7 @@ Urgent
|
||||
DR. [NOM]
|
||||
Voie d`administration : SOUS-CUTANEE
|
||||
Statut des prescriptions : Signé
|
||||
[NOM] FLEXPEN
|
||||
LEVEMIR FLEXPEN
|
||||
300U/3ML SOL INJ STY [5]
|
||||
Cartouche(s)
|
||||
10 U
|
||||
@@ -1436,19 +1436,19 @@ Signé — SERESTA 10MG CPR - 10MG
|
||||
comprime - Dose 1 CPR - ORALE - Nuit [21h] Si
|
||||
besoin - Début presc. : 13/11/2023 @ 12:18
|
||||
"si anxiete "
|
||||
Signé — VOGALENE [NOM] 7,5MG
|
||||
Signé — VOGALENE LYOC 7,5MG
|
||||
LYOPHILISAT ORAL - 7,5MG lyophilisat - Dose
|
||||
1 LYOPHILISAT(S) - ORALE - Toutes les 8
|
||||
Heure(s) Si besoin - Début presc. : 13/11/2023 @
|
||||
12 : 30
|
||||
si nausée
|
||||
Signé — VOGALENE [NOM] 7,5MG
|
||||
Signé — VOGALENE LYOC 7,5MG
|
||||
LYOPHILISAT ORAL - 7,5MG lyophilisat - Dose
|
||||
1 LYOPHILISAT(S) - ORALE - Toutes les 8
|
||||
Heure(s) Si besoin - Début presc. : 13/11/2023 @
|
||||
12 : 30
|
||||
si nausée
|
||||
Signé — VOGALENE [NOM] 7,5MG
|
||||
Signé — VOGALENE LYOC 7,5MG
|
||||
LYOPHILISAT ORAL - 7,5MG lyophilisat - Dose
|
||||
1 LYOPHILISAT(S) - ORALE - Toutes les 8
|
||||
Heure(s) Si besoin - Début presc. : 13/11/2023 @
|
||||
@@ -2045,19 +2045,19 @@ Signé — SERESTA 10MG CPR - 10MG
|
||||
comprime - Dose 1 CPR - ORALE - Nuit [21h] Si
|
||||
besoin - Début presc. : 13/11/2023 @ 12:18
|
||||
"si anxiete "
|
||||
Signé — VOGALENE [NOM] 7,5MG
|
||||
Signé — VOGALENE LYOC 7,5MG
|
||||
LYOPHILISAT ORAL - 7,5MG lyophilisat - Dose
|
||||
1 LYOPHILISAT(S) - ORALE - Toutes les 8
|
||||
Heure(s) Si besoin - Début presc. : 13/11/2023 @
|
||||
12 : 30
|
||||
si nausée
|
||||
Signé — VOGALENE [NOM] 7,5MG
|
||||
Signé — VOGALENE LYOC 7,5MG
|
||||
LYOPHILISAT ORAL - 7,5MG lyophilisat - Dose
|
||||
1 LYOPHILISAT(S) - ORALE - Toutes les 8
|
||||
Heure(s) Si besoin - Début presc. : 13/11/2023 @
|
||||
12 : 30
|
||||
si nausée
|
||||
Signé — VOGALENE [NOM] 7,5MG
|
||||
Signé — VOGALENE LYOC 7,5MG
|
||||
LYOPHILISAT ORAL - 7,5MG lyophilisat - Dose
|
||||
1 LYOPHILISAT(S) - ORALE - Toutes les 8
|
||||
Heure(s) Si besoin - Début presc. : 13/11/2023 @
|
||||
|
||||
@@ -11,6 +11,7 @@ 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
|
||||
from gliner_manager import GlinerManager
|
||||
|
||||
SRC = Path("/home/dom/Téléchargements/II-1 Ctrl_T2A_2025_CHCB_DocJustificatifs (1)")
|
||||
OUTDIR = SRC / "anonymise_audit_30"
|
||||
@@ -57,6 +58,15 @@ def main():
|
||||
assert ner.is_loaded(), "EDS-Pseudo non chargé"
|
||||
print("EDS-Pseudo chargé.", flush=True)
|
||||
|
||||
print("Chargement GLiNER (vote croisé NER)...", flush=True)
|
||||
gliner = GlinerManager()
|
||||
try:
|
||||
gliner.load()
|
||||
print("GLiNER chargé.", flush=True)
|
||||
except Exception as e:
|
||||
print(f"GLiNER indisponible ({e}), on continue sans.", flush=True)
|
||||
gliner = None
|
||||
|
||||
print("Chargement VLM (Ollama qwen2.5vl:7b)...", flush=True)
|
||||
vlm = VlmManager()
|
||||
try:
|
||||
@@ -97,6 +107,7 @@ def main():
|
||||
ner_thresholds=None,
|
||||
ogc_label=ogc,
|
||||
vlm_manager=vlm,
|
||||
gliner_manager=gliner,
|
||||
)
|
||||
audit_path = Path(outputs.get("audit", ""))
|
||||
if audit_path.exists():
|
||||
|
||||
145
scripts/export_silver_annotations.py
Normal file
145
scripts/export_silver_annotations.py
Normal file
@@ -0,0 +1,145 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Export silver annotations — Génère des données d'entraînement BIO à partir du pipeline existant.
|
||||
================================================================================================
|
||||
Utilise le pipeline regex+NER+VLM actuel pour produire des annotations "silver standard"
|
||||
sur les 706 OGC. Ces annotations servent de base pour fine-tuner CamemBERT-bio.
|
||||
|
||||
Usage:
|
||||
python scripts/export_silver_annotations.py [--limit N] [--out-dir DIR]
|
||||
|
||||
Output: data/silver_annotations/ avec un fichier .bio par document
|
||||
Format BIO: TOKEN\tLABEL (un token par ligne, lignes vides entre phrases)
|
||||
"""
|
||||
import sys
|
||||
import re
|
||||
import json
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
from typing import List, Tuple
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
# Regex pour détecter les placeholders et reconstruire l'alignement
|
||||
PLACEHOLDER_RE = re.compile(
|
||||
r"\[(NOM|TEL|EMAIL|NIR|IPP|DOSSIER|NDA|EPISODE|RPPS|DATE_NAISSANCE|"
|
||||
r"ADRESSE|CODE_POSTAL|VILLE|MASK|FINESS|OGC|AGE|ETAB|IBAN)\]"
|
||||
)
|
||||
|
||||
# Mapping placeholder → label BIO
|
||||
PH_TO_BIO = {
|
||||
"NOM": "PER",
|
||||
"TEL": "TEL",
|
||||
"EMAIL": "EMAIL",
|
||||
"NIR": "NIR",
|
||||
"IPP": "IPP",
|
||||
"DOSSIER": "NDA",
|
||||
"NDA": "NDA",
|
||||
"EPISODE": "NDA",
|
||||
"RPPS": "RPPS",
|
||||
"DATE_NAISSANCE": "DATE_NAISSANCE",
|
||||
"ADRESSE": "ADRESSE",
|
||||
"CODE_POSTAL": "ZIP",
|
||||
"VILLE": "VILLE",
|
||||
"ETAB": "HOPITAL",
|
||||
"FINESS": "HOPITAL",
|
||||
"IBAN": "IBAN",
|
||||
"AGE": "AGE",
|
||||
"OGC": "NDA",
|
||||
"MASK": "O", # MASK générique = pas d'annotation spécifique
|
||||
}
|
||||
|
||||
|
||||
def text_to_bio(pseudonymised_text: str) -> List[Tuple[str, str]]:
|
||||
"""Convertit un texte pseudonymisé en séquence BIO.
|
||||
|
||||
Les tokens [PLACEHOLDER] deviennent B-TYPE / I-TYPE.
|
||||
Les tokens normaux deviennent O.
|
||||
"""
|
||||
bio_tokens: List[Tuple[str, str]] = []
|
||||
|
||||
# Split le texte en segments : alternance texte normal / placeholder
|
||||
parts = PLACEHOLDER_RE.split(pseudonymised_text)
|
||||
# parts = [texte, label, texte, label, texte, ...]
|
||||
|
||||
i = 0
|
||||
while i < len(parts):
|
||||
if i % 2 == 0:
|
||||
# Texte normal
|
||||
text_part = parts[i]
|
||||
for word in text_part.split():
|
||||
word = word.strip()
|
||||
if word:
|
||||
bio_tokens.append((word, "O"))
|
||||
else:
|
||||
# Label de placeholder
|
||||
label = parts[i]
|
||||
bio_label = PH_TO_BIO.get(label, "O")
|
||||
if bio_label != "O":
|
||||
# Le placeholder remplace un ou plusieurs tokens
|
||||
bio_tokens.append((f"[{label}]", f"B-{bio_label}"))
|
||||
else:
|
||||
bio_tokens.append((f"[{label}]", "O"))
|
||||
i += 1
|
||||
|
||||
return bio_tokens
|
||||
|
||||
|
||||
def export_document(pseudo_path: Path, out_dir: Path) -> int:
|
||||
"""Exporte un fichier pseudonymisé en format BIO. Retourne le nombre de tokens."""
|
||||
text = pseudo_path.read_text(encoding="utf-8", errors="replace")
|
||||
|
||||
bio_tokens = text_to_bio(text)
|
||||
if not bio_tokens:
|
||||
return 0
|
||||
|
||||
# Écrire en format CoNLL (TOKEN\tLABEL)
|
||||
out_path = out_dir / pseudo_path.name.replace(".pseudonymise.txt", ".bio")
|
||||
lines = []
|
||||
for token, label in bio_tokens:
|
||||
# Séparer les "phrases" par des lignes vides (heuristique: point final ou retour ligne)
|
||||
if token in (".", "!", "?") and label == "O":
|
||||
lines.append(f"{token}\t{label}")
|
||||
lines.append("") # séparateur de phrase
|
||||
else:
|
||||
lines.append(f"{token}\t{label}")
|
||||
|
||||
out_path.write_text("\n".join(lines), encoding="utf-8")
|
||||
return len(bio_tokens)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Export silver annotations BIO")
|
||||
parser.add_argument("--input-dir", type=Path,
|
||||
default=Path("/home/dom/Téléchargements/II-1 Ctrl_T2A_2025_CHCB_DocJustificatifs (1)/anonymise_audit_30"),
|
||||
help="Répertoire contenant les .pseudonymise.txt")
|
||||
parser.add_argument("--out-dir", type=Path,
|
||||
default=Path(__file__).parent.parent / "data" / "silver_annotations",
|
||||
help="Répertoire de sortie")
|
||||
parser.add_argument("--limit", type=int, default=0, help="Limiter à N fichiers (0=tous)")
|
||||
args = parser.parse_args()
|
||||
|
||||
args.out_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
pseudo_files = sorted(args.input_dir.glob("*.pseudonymise.txt"))
|
||||
if args.limit > 0:
|
||||
pseudo_files = pseudo_files[:args.limit]
|
||||
|
||||
print(f"Export silver annotations: {len(pseudo_files)} fichiers → {args.out_dir}")
|
||||
|
||||
total_tokens = 0
|
||||
total_entities = 0
|
||||
for f in pseudo_files:
|
||||
n = export_document(f, args.out_dir)
|
||||
ent_count = sum(1 for line in (args.out_dir / f.name.replace(".pseudonymise.txt", ".bio")).read_text().splitlines()
|
||||
if line and not line.endswith("\tO"))
|
||||
total_tokens += n
|
||||
total_entities += ent_count
|
||||
print(f" {f.name}: {n} tokens, {ent_count} entités")
|
||||
|
||||
print(f"\nTotal: {total_tokens} tokens, {total_entities} entités annotées")
|
||||
print(f"Sortie: {args.out_dir}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
256
scripts/finetune_camembert_bio.py
Normal file
256
scripts/finetune_camembert_bio.py
Normal file
@@ -0,0 +1,256 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Fine-tune CamemBERT-bio pour la désidentification clinique française.
|
||||
=====================================================================
|
||||
Entraîne almanach/camembert-bio-base sur les annotations silver/gold
|
||||
exportées par export_silver_annotations.py.
|
||||
|
||||
Usage:
|
||||
python scripts/finetune_camembert_bio.py [--epochs 5] [--batch-size 8] [--lr 2e-5]
|
||||
|
||||
Prérequis: pip install transformers datasets seqeval accelerate
|
||||
Export ONNX post-training: python scripts/export_onnx.py
|
||||
"""
|
||||
import sys
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
from typing import Dict, List
|
||||
|
||||
import numpy as np
|
||||
|
||||
# Vérifier les dépendances
|
||||
try:
|
||||
from transformers import (
|
||||
AutoTokenizer,
|
||||
AutoModelForTokenClassification,
|
||||
TrainingArguments,
|
||||
Trainer,
|
||||
DataCollatorForTokenClassification,
|
||||
)
|
||||
from datasets import Dataset, DatasetDict
|
||||
import evaluate
|
||||
except ImportError as e:
|
||||
print(f"Dépendance manquante: {e}")
|
||||
print("Installez: pip install transformers datasets seqeval accelerate")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
# Labels BIO pour la désidentification
|
||||
LABEL_LIST = [
|
||||
"O",
|
||||
"B-PER", "I-PER",
|
||||
"B-TEL", "I-TEL",
|
||||
"B-EMAIL", "I-EMAIL",
|
||||
"B-NIR", "I-NIR",
|
||||
"B-IPP", "I-IPP",
|
||||
"B-NDA", "I-NDA",
|
||||
"B-RPPS", "I-RPPS",
|
||||
"B-DATE_NAISSANCE", "I-DATE_NAISSANCE",
|
||||
"B-ADRESSE", "I-ADRESSE",
|
||||
"B-ZIP", "I-ZIP",
|
||||
"B-VILLE", "I-VILLE",
|
||||
"B-HOPITAL", "I-HOPITAL",
|
||||
"B-IBAN", "I-IBAN",
|
||||
"B-AGE", "I-AGE",
|
||||
]
|
||||
LABEL2ID = {l: i for i, l in enumerate(LABEL_LIST)}
|
||||
ID2LABEL = {i: l for l, i in LABEL2ID.items()}
|
||||
|
||||
MODEL_NAME = "almanach/camembert-bio-base"
|
||||
|
||||
|
||||
def load_bio_files(data_dir: Path) -> Dict[str, List]:
|
||||
"""Charge les fichiers .bio en format HuggingFace datasets."""
|
||||
tokens_list: List[List[str]] = []
|
||||
labels_list: List[List[int]] = []
|
||||
|
||||
for bio_file in sorted(data_dir.glob("*.bio")):
|
||||
text = bio_file.read_text(encoding="utf-8")
|
||||
current_tokens: List[str] = []
|
||||
current_labels: List[int] = []
|
||||
|
||||
for line in text.splitlines():
|
||||
line = line.strip()
|
||||
if not line:
|
||||
# Fin de phrase
|
||||
if current_tokens:
|
||||
tokens_list.append(current_tokens)
|
||||
labels_list.append(current_labels)
|
||||
current_tokens = []
|
||||
current_labels = []
|
||||
continue
|
||||
|
||||
parts = line.split("\t")
|
||||
if len(parts) != 2:
|
||||
continue
|
||||
token, label = parts
|
||||
label_id = LABEL2ID.get(label, LABEL2ID["O"])
|
||||
current_tokens.append(token)
|
||||
current_labels.append(label_id)
|
||||
|
||||
if current_tokens:
|
||||
tokens_list.append(current_tokens)
|
||||
labels_list.append(current_labels)
|
||||
|
||||
return {"tokens": tokens_list, "ner_tags": labels_list}
|
||||
|
||||
|
||||
def tokenize_and_align(examples, tokenizer):
|
||||
"""Tokenize et aligne les labels avec les sous-tokens."""
|
||||
tokenized = tokenizer(
|
||||
examples["tokens"],
|
||||
truncation=True,
|
||||
is_split_into_words=True,
|
||||
max_length=512,
|
||||
padding=False,
|
||||
)
|
||||
|
||||
all_labels = []
|
||||
for i, labels in enumerate(examples["ner_tags"]):
|
||||
word_ids = tokenized.word_ids(batch_index=i)
|
||||
label_ids = []
|
||||
prev_word_id = None
|
||||
for word_id in word_ids:
|
||||
if word_id is None:
|
||||
label_ids.append(-100)
|
||||
elif word_id != prev_word_id:
|
||||
label_ids.append(labels[word_id])
|
||||
else:
|
||||
# Sous-token : I- si le premier est B-, sinon même label
|
||||
orig = labels[word_id]
|
||||
if orig > 0 and LABEL_LIST[orig].startswith("B-"):
|
||||
# Convertir B- en I-
|
||||
i_label = LABEL_LIST[orig].replace("B-", "I-")
|
||||
label_ids.append(LABEL2ID.get(i_label, orig))
|
||||
else:
|
||||
label_ids.append(orig)
|
||||
prev_word_id = word_id
|
||||
all_labels.append(label_ids)
|
||||
|
||||
tokenized["labels"] = all_labels
|
||||
return tokenized
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Fine-tune CamemBERT-bio pour désidentification")
|
||||
parser.add_argument("--data-dir", type=Path,
|
||||
default=Path(__file__).parent.parent / "data" / "silver_annotations",
|
||||
help="Répertoire des fichiers .bio")
|
||||
parser.add_argument("--output-dir", type=Path,
|
||||
default=Path(__file__).parent.parent / "models" / "camembert-bio-deid",
|
||||
help="Répertoire de sortie du modèle")
|
||||
parser.add_argument("--epochs", type=int, default=5)
|
||||
parser.add_argument("--batch-size", type=int, default=8)
|
||||
parser.add_argument("--lr", type=float, default=2e-5)
|
||||
parser.add_argument("--val-split", type=float, default=0.15, help="Fraction pour validation")
|
||||
args = parser.parse_args()
|
||||
|
||||
# Charger les données
|
||||
print(f"Chargement des données depuis {args.data_dir}...")
|
||||
raw_data = load_bio_files(args.data_dir)
|
||||
n_sentences = len(raw_data["tokens"])
|
||||
n_entities = sum(1 for labels in raw_data["ner_tags"] for l in labels if l != 0)
|
||||
print(f" {n_sentences} phrases, {n_entities} entités annotées")
|
||||
|
||||
if n_sentences < 10:
|
||||
print("ERREUR: pas assez de données. Lancez d'abord export_silver_annotations.py")
|
||||
sys.exit(1)
|
||||
|
||||
# Split train/val
|
||||
dataset = Dataset.from_dict(raw_data)
|
||||
split = dataset.train_test_split(test_size=args.val_split, seed=42)
|
||||
datasets = DatasetDict({"train": split["train"], "validation": split["test"]})
|
||||
print(f" Train: {len(datasets['train'])}, Validation: {len(datasets['validation'])}")
|
||||
|
||||
# Tokenizer + modèle
|
||||
print(f"\nChargement du modèle {MODEL_NAME}...")
|
||||
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
|
||||
model = AutoModelForTokenClassification.from_pretrained(
|
||||
MODEL_NAME,
|
||||
num_labels=len(LABEL_LIST),
|
||||
id2label=ID2LABEL,
|
||||
label2id=LABEL2ID,
|
||||
)
|
||||
|
||||
# Tokenization
|
||||
tokenized = datasets.map(
|
||||
lambda ex: tokenize_and_align(ex, tokenizer),
|
||||
batched=True,
|
||||
remove_columns=datasets["train"].column_names,
|
||||
)
|
||||
|
||||
# Métriques
|
||||
seqeval = evaluate.load("seqeval")
|
||||
|
||||
def compute_metrics(eval_pred):
|
||||
logits, labels = eval_pred
|
||||
predictions = np.argmax(logits, axis=-1)
|
||||
true_labels = []
|
||||
true_preds = []
|
||||
for pred_seq, label_seq in zip(predictions, labels):
|
||||
t_labels = []
|
||||
t_preds = []
|
||||
for p, l in zip(pred_seq, label_seq):
|
||||
if l != -100:
|
||||
t_labels.append(LABEL_LIST[l])
|
||||
t_preds.append(LABEL_LIST[p])
|
||||
true_labels.append(t_labels)
|
||||
true_preds.append(t_preds)
|
||||
results = seqeval.compute(predictions=true_preds, references=true_labels)
|
||||
return {
|
||||
"precision": results["overall_precision"],
|
||||
"recall": results["overall_recall"],
|
||||
"f1": results["overall_f1"],
|
||||
}
|
||||
|
||||
# Training
|
||||
args.output_dir.mkdir(parents=True, exist_ok=True)
|
||||
training_args = TrainingArguments(
|
||||
output_dir=str(args.output_dir),
|
||||
num_train_epochs=args.epochs,
|
||||
per_device_train_batch_size=args.batch_size,
|
||||
per_device_eval_batch_size=args.batch_size * 2,
|
||||
learning_rate=args.lr,
|
||||
weight_decay=0.01,
|
||||
warmup_ratio=0.1,
|
||||
eval_strategy="epoch",
|
||||
save_strategy="epoch",
|
||||
load_best_model_at_end=True,
|
||||
metric_for_best_model="f1",
|
||||
logging_steps=50,
|
||||
fp16=False, # CPU training
|
||||
report_to="none",
|
||||
save_total_limit=2,
|
||||
)
|
||||
|
||||
data_collator = DataCollatorForTokenClassification(tokenizer)
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=tokenized["train"],
|
||||
eval_dataset=tokenized["validation"],
|
||||
data_collator=data_collator,
|
||||
compute_metrics=compute_metrics,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
|
||||
print(f"\nDémarrage du fine-tuning ({args.epochs} epochs, batch={args.batch_size}, lr={args.lr})...")
|
||||
trainer.train()
|
||||
|
||||
# Sauvegarder
|
||||
trainer.save_model(str(args.output_dir / "best"))
|
||||
tokenizer.save_pretrained(str(args.output_dir / "best"))
|
||||
print(f"\nModèle sauvegardé: {args.output_dir / 'best'}")
|
||||
|
||||
# Évaluation finale
|
||||
results = trainer.evaluate()
|
||||
print(f"\nRésultats finaux:")
|
||||
print(f" Precision: {results['eval_precision']:.4f}")
|
||||
print(f" Recall: {results['eval_recall']:.4f}")
|
||||
print(f" F1: {results['eval_f1']:.4f}")
|
||||
print(f"\nPour exporter en ONNX:")
|
||||
print(f" python -m optimum.exporters.onnx --model {args.output_dir / 'best'} {args.output_dir / 'onnx'}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user