feat: Phase 4 — viewer enrichi, non-cumul CCAM, fusion multi-PDFs + rebuild FAISS (21 141 vecteurs)
- Viewer : badges compteurs (DAS, actes, alertes, CMA), raisonnement LLM pliable, regroupement CCAM, navigation patient, alertes NON-CUMUL en rouge - Non-cumul CCAM : 3 règles heuristiques (même base, même regroupement/jour, paires incompatibles) - Fusion multi-PDFs : merge_dossiers() avec priorité Trackare, spécificité CIM-10, déduplication, champ source_files - Index FAISS reconstruit : 21 141 vecteurs (CCAM dict 8 257 + CIM-10 alpha 306) - 192 tests unitaires passent Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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src/medical/fusion.py
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246
src/medical/fusion.py
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"""Fusion de dossiers médicaux multi-PDFs pour un même patient.
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Combine les informations de plusieurs documents (Trackare, CRH, CRO) en un
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dossier unique avec des règles de priorité et de déduplication.
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"""
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from __future__ import annotations
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import logging
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from ..config import (
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ActeCCAM,
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BiologieCle,
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Diagnostic,
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DossierMedical,
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Imagerie,
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Sejour,
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Traitement,
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)
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logger = logging.getLogger(__name__)
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# Priorité des types de documents pour les données de séjour
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_DOC_PRIORITY = {"trackare": 0, "crh": 1, "cro": 2}
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def _cim10_specificity(code: str | None) -> int:
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"""Score de spécificité d'un code CIM-10 : longueur sans le point."""
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if not code:
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return 0
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return len(code.replace(".", ""))
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def _prefer_most_specific_dp(dossiers: list[DossierMedical]) -> Diagnostic | None:
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"""Sélectionne le DP le plus spécifique parmi tous les dossiers."""
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candidates: list[tuple[Diagnostic, int]] = []
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for d in dossiers:
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if d.diagnostic_principal:
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spec = _cim10_specificity(d.diagnostic_principal.cim10_suggestion)
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candidates.append((d.diagnostic_principal, spec))
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if not candidates:
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return None
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# Tri : spécificité décroissante, puis confiance (high > medium > low)
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conf_order = {"high": 0, "medium": 1, "low": 2}
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candidates.sort(
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key=lambda x: (-x[1], conf_order.get(x[0].cim10_confidence or "", 3))
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)
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return candidates[0][0]
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def _merge_sejour(dossiers: list[DossierMedical]) -> Sejour:
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"""Fusionne les informations de séjour avec priorité Trackare > CRH > CRO."""
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# Trier par priorité de type de document
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sorted_dossiers = sorted(
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dossiers,
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key=lambda d: _DOC_PRIORITY.get(d.document_type, 99),
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)
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merged = Sejour()
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for d in sorted_dossiers:
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s = d.sejour
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if s.sexe and not merged.sexe:
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merged.sexe = s.sexe
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if s.age is not None and merged.age is None:
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merged.age = s.age
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if s.date_entree and not merged.date_entree:
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merged.date_entree = s.date_entree
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if s.date_sortie and not merged.date_sortie:
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merged.date_sortie = s.date_sortie
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if s.duree_sejour is not None and merged.duree_sejour is None:
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merged.duree_sejour = s.duree_sejour
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if s.mode_entree and not merged.mode_entree:
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merged.mode_entree = s.mode_entree
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if s.mode_sortie and not merged.mode_sortie:
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merged.mode_sortie = s.mode_sortie
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if s.imc is not None and merged.imc is None:
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merged.imc = s.imc
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if s.poids is not None and merged.poids is None:
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merged.poids = s.poids
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if s.taille is not None and merged.taille is None:
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merged.taille = s.taille
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return merged
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def _dedup_diagnostics(all_das: list[Diagnostic]) -> list[Diagnostic]:
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"""Déduplique les diagnostics associés par code CIM-10, garde la meilleure confiance."""
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conf_order = {"high": 0, "medium": 1, "low": 2}
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seen: dict[str | None, Diagnostic] = {}
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for d in all_das:
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key = d.cim10_suggestion
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if key is None:
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# Sans code, dédup par texte normalisé
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key = f"__text__{d.texte.lower().strip()}"
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if key not in seen:
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seen[key] = d
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else:
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existing = seen[key]
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# Garder celui avec la meilleure confiance
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if conf_order.get(d.cim10_confidence or "", 3) < conf_order.get(
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existing.cim10_confidence or "", 3
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):
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seen[key] = d
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return list(seen.values())
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def _dedup_actes(all_actes: list[ActeCCAM]) -> list[ActeCCAM]:
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"""Déduplique les actes CCAM par code."""
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seen: dict[str | None, ActeCCAM] = {}
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for a in all_actes:
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key = a.code_ccam_suggestion
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if key is None:
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key = f"__text__{a.texte.lower().strip()}"
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if key not in seen:
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seen[key] = a
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else:
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existing = seen[key]
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# Garder celui avec date si possible
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if a.date and not existing.date:
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seen[key] = a
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return list(seen.values())
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def merge_dossiers(dossiers: list[DossierMedical]) -> DossierMedical:
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"""Fusionne plusieurs dossiers médicaux d'un même patient.
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Args:
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dossiers: Liste de DossierMedical issus de PDFs différents.
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Returns:
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Un DossierMedical fusionné.
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"""
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if len(dossiers) == 1:
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result = dossiers[0].model_copy(deep=True)
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result.source_files = [result.source_file]
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return result
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merged = DossierMedical()
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# Source files
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merged.source_files = [d.source_file for d in dossiers if d.source_file]
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# Séjour
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merged.sejour = _merge_sejour(dossiers)
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# Diagnostic principal : le plus spécifique
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merged.diagnostic_principal = _prefer_most_specific_dp(dossiers)
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# Collecter tous les DAS + DP non retenus comme DAS
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all_das: list[Diagnostic] = []
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for d in dossiers:
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all_das.extend(d.diagnostics_associes)
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# Si le DP de ce dossier est différent du DP fusionné, l'ajouter comme DAS
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if (
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d.diagnostic_principal
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and merged.diagnostic_principal
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and d.diagnostic_principal.cim10_suggestion
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!= merged.diagnostic_principal.cim10_suggestion
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):
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all_das.append(d.diagnostic_principal)
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merged.diagnostics_associes = _dedup_diagnostics(all_das)
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# Actes CCAM
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all_actes: list[ActeCCAM] = []
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for d in dossiers:
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all_actes.extend(d.actes_ccam)
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merged.actes_ccam = _dedup_actes(all_actes)
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# Biologie : union, dédup par (test, valeur)
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bio_seen: set[tuple[str, str | None]] = set()
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for d in dossiers:
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for b in d.biologie_cle:
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key = (b.test, b.valeur)
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if key not in bio_seen:
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merged.biologie_cle.append(b)
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bio_seen.add(key)
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# Imagerie : union, dédup par (type, conclusion)
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img_seen: set[tuple[str, str | None]] = set()
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for d in dossiers:
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for i in d.imagerie:
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key = (i.type, i.conclusion)
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if key not in img_seen:
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merged.imagerie.append(i)
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img_seen.add(key)
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# Traitements : union, dédup par médicament (normalisé)
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med_seen: set[str] = set()
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for d in dossiers:
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for t in d.traitements_sortie:
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key = t.medicament.lower().strip()
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if key not in med_seen:
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merged.traitements_sortie.append(t)
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med_seen.add(key)
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# Antécédents : union, dédup par texte normalisé
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ant_seen: set[str] = set()
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for d in dossiers:
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for a in d.antecedents:
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key = a.lower().strip()
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if key not in ant_seen:
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merged.antecedents.append(a)
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ant_seen.add(key)
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# Complications : union, dédup par texte normalisé
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comp_seen: set[str] = set()
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for d in dossiers:
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for c in d.complications:
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key = c.lower().strip()
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if key not in comp_seen:
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merged.complications.append(c)
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comp_seen.add(key)
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# Alertes : alerte de fusion en tête + union
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merged.alertes_codage = [f"FUSION: {len(dossiers)} documents fusionnés"]
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alert_seen: set[str] = set()
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for d in dossiers:
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for a in d.alertes_codage:
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if a not in alert_seen:
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merged.alertes_codage.append(a)
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alert_seen.add(a)
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# Document type : le type prioritaire
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sorted_by_prio = sorted(
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dossiers,
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key=lambda d: _DOC_PRIORITY.get(d.document_type, 99),
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)
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merged.document_type = sorted_by_prio[0].document_type
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logger.info(
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"Fusion de %d dossiers : DP=%s, %d DAS, %d actes",
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len(dossiers),
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merged.diagnostic_principal.cim10_suggestion if merged.diagnostic_principal else "aucun",
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len(merged.diagnostics_associes),
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len(merged.actes_ccam),
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)
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return merged
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