40934fdc39
Ajoute source_page/source_excerpt à tous les types (biologie, imagerie, traitements, actes CCAM, antécédents, complications). Convertit antecedents et complications en types structurés (Antecedent/Complication) avec validators backward-compat pour les vieux JSON. Étend _apply_source_tracking à tous les éléments du dossier. Ajoute un endpoint /api/source-text/ et un modal interactif dans le viewer avec surlignage du texte source. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
295 lines
10 KiB
Python
295 lines
10 KiB
Python
"""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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Antecedent,
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BiologieCle,
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Complication,
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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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from ..medical.das_filter import is_valid_diagnostic_text, apply_semantic_dedup
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from ..medical.cim10_extractor import _is_dp_family_redundant
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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 _is_enriched(d: Diagnostic) -> bool:
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"""Retourne True si le diagnostic a une justification RAG."""
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return bool(d.justification or d.sources_rag)
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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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new_conf = conf_order.get(d.cim10_confidence or "", 3)
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old_conf = conf_order.get(existing.cim10_confidence or "", 3)
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# Garder celui avec la meilleure confiance, ou à confiance égale celui enrichi
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if new_conf < old_conf or (new_conf == old_conf and _is_enriched(d) and not _is_enriched(existing)):
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seen[key] = d
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# Supprimer les codes parents quand un code plus spécifique existe
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# Ex: K85 retiré si K85.9 présent (K85 est préfixe strict de K859)
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codes = {k for k in seen if k and not k.startswith("__text__")}
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normalized = {c: c.replace(".", "") for c in codes}
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parents_to_remove: set[str] = set()
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for code_a in codes:
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norm_a = normalized[code_a]
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for code_b in codes:
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if code_a == code_b:
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continue
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norm_b = normalized[code_b]
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if norm_b.startswith(norm_a) and len(norm_b) > len(norm_a):
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parents_to_remove.add(code_a)
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break
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for parent in parents_to_remove:
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del seen[parent]
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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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# Appliquer la dédup famille DP + sémantique même pour un seul dossier
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dp_code = result.diagnostic_principal.cim10_suggestion if result.diagnostic_principal else None
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if dp_code:
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result.diagnostics_associes = [
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d for d in result.diagnostics_associes
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if not d.cim10_suggestion or not _is_dp_family_redundant(d.cim10_suggestion, dp_code)
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]
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result.diagnostics_associes = apply_semantic_dedup(result.diagnostics_associes)
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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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# mais seulement si le texte est un diagnostic valide (filtre artefacts OCR)
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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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and is_valid_diagnostic_text(d.diagnostic_principal.texte)
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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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# Retirer les DAS redondants avec le DP (même code, famille, parent/enfant)
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dp_code = merged.diagnostic_principal.cim10_suggestion if merged.diagnostic_principal else None
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if dp_code:
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merged.diagnostics_associes = [
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d for d in merged.diagnostics_associes
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if not d.cim10_suggestion or not _is_dp_family_redundant(d.cim10_suggestion, dp_code)
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]
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# Redondances sémantiques entre DAS
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merged.diagnostics_associes = apply_semantic_dedup(merged.diagnostics_associes)
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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.texte.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.texte.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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