refactor: split cim10_extractor → bio_normals, bio_extraction, diagnostic_extraction, validation_pipeline
Découpe le monolithe cim10_extractor.py (1356L) en 4 modules spécialisés : - bio_normals.py : constante BIO_NORMALS + _is_abnormal() (feuille) - bio_extraction.py : extraction biologie structurée - diagnostic_extraction.py : extraction DP/DAS/actes CCAM - validation_pipeline.py : validation CIM-10/CCAM + règles métier Le cim10_extractor.py reste orchestrateur (~450L) avec re-exports backward-compat. Imports mis à jour dans clinical_context, rag_search, fusion. 748 tests passent. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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dom
184
src/medical/bio_extraction.py
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184
src/medical/bio_extraction.py
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@@ -0,0 +1,184 @@
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"""Extraction des résultats biologiques depuis le texte médical."""
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from __future__ import annotations
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import re
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import unicodedata
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from ..config import BiologieCle, DossierMedical, load_lab_value_sanity
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from .bio_normals import BIO_NORMALS, _is_abnormal
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def _norm_key(s: str) -> str:
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"""Normalise une clé (minuscules, sans accents) pour index YAML."""
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s = (s or "").strip().lower()
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s = unicodedata.normalize("NFKD", s)
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s = "".join(ch for ch in s if not unicodedata.combining(ch))
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return re.sub(r"\s+", " ", s)
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def _parse_float_and_token(raw: str) -> tuple[float | None, str | None]:
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"""Parse un float et renvoie aussi le token numérique normalisé (avec '.')."""
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if raw is None:
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return None, None
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s = str(raw).strip()
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m = re.search(r"(-?\d+(?:[\.,]\d+)?)", s)
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if not m:
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return None, None
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token = m.group(1).replace(",", ".")
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try:
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return float(token), token
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except ValueError:
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return None, None
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def _sanitize_bio_value(test_name: str, raw_value: str, sanity_cfg: dict) -> tuple[str, float, str, str | None] | None:
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"""Applique des garde-fous anti-artefacts (OCR/PDF).
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Retour:
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(token, value_float, quality, reason) ou None si non parsable.
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quality: ok | suspect | discarded
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"""
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val, token = _parse_float_and_token(raw_value)
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if val is None or token is None:
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return None
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key = _norm_key(test_name)
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tests_cfg = (sanity_cfg or {}).get("tests") or {}
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cfg = tests_cfg.get(key) or {}
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hard_min = cfg.get("hard_min")
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hard_max = cfg.get("hard_max")
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if hard_min is not None and val < float(hard_min):
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return token, val, "discarded", f"Valeur hors bornes plausibles (<{hard_min})"
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if hard_max is not None and val > float(hard_max):
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return token, val, "discarded", f"Valeur hors bornes plausibles (>{hard_max})"
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quality = "ok"
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reason: str | None = None
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suspect_cfg = cfg.get("suspect") or {}
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single_digit_over = suspect_cfg.get("single_digit_over")
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if single_digit_over is not None:
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# Ex: potassium '8' au lieu de '4.8' (décimale perdue)
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if re.fullmatch(r"\d", str(raw_value).strip()) and val >= float(single_digit_over):
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quality = "suspect"
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reason = f"Valeur à 1 chiffre (possible décimale perdue) : vérifier dans le CR"
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return token, val, quality, reason
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def _extract_biologie(text: str, dossier: DossierMedical) -> None:
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"""Extrait des résultats biologiques clés.
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Notes:
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- Supporte des aliases (TGO/TGP, Hb, Na/K…)
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- Capte plusieurs occurrences (utile pour valider/infirmer des diagnostics)
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- Reste volontairement *simple* (regex sur texte extrait) : si une valeur est
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uniquement dans un tableau PDF mal extrait, elle peut manquer.
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"""
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# (pattern, test_name)
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bio_patterns: list[tuple[str, str]] = [
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(r"[Ll]ipas[ée]mie\s*(?:[àa=:])?\s*(\d+)\s*(?:UI/L|U/L)?", "Lipasémie"),
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(r"\bCRP\b\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:mg/[Ll])?", "CRP"),
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(r"(?:\bASAT\b|\bTGO\b)\s*[=:àa]?\s*([\d.,]+)\s*(?:N|U(?:I)?/L)?", "ASAT"),
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(r"(?:\bALAT\b|\bTGP\b)\s*[=:àa]?\s*([\d.,]+)\s*(?:N|U(?:I)?/L)?", "ALAT"),
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(r"\bGGT\b\s*[=:àa]?\s*(\d+)\s*(?:U(?:I)?/L)?", "GGT"),
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(r"\bPAL\b\s*[=:àa]?\s*(\d+)\s*(?:U(?:I)?/L)?", "PAL"),
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(r"[Bb]ilirubine\s+(?:totale\s+)?[àa=:]\s*(\d+(?:[.,]\d+)?)\s*(?:µmol/L|mg/dL)?", "Bilirubine totale"),
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# Ionogramme / électrolytes
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(r"(?:[Ss]odium|[Nn]atr[ée]mie|(?<![A-Za-z])Na\+?(?![A-Za-z]))\s*[=:àa]?\s*([0-9]{2,3}(?:[.,][0-9]+)?)\s*(?:mmol/L|mEq/L)?", "Sodium"),
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(r"(?:[Pp]otassium|[Kk]ali[ée]mie|(?<![A-Za-z])K\+?(?![A-Za-z]))\s*[=:àa]?\s*([0-9](?:[.,][0-9]+)?)\s*(?:mmol/L|mEq/L)?", "Potassium"),
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(r"[Tt]roponine\s+(?:us\s+)?(n[ée]gative|positive|normale)", "Troponine"),
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(r"(?:[Hh][ée]moglobine|\bHb\b)\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:g/dL|g/L)?", "Hémoglobine"),
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(r"[Pp]laquettes?\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:/mm3|G/L)?", "Plaquettes"),
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(r"[Ll]eucocytes?\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:/mm3|G/L)?", "Leucocytes"),
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(r"[Cc]r[ée]atinine?\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:µmol/L|mg/dL)?", "Créatinine"),
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]
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# Anti-doublons + limite par test (évite d'exploser le JSON)
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max_per_test = 6
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counts: dict[str, int] = {}
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seen: set[tuple[str, str]] = set()
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sanity_cfg = load_lab_value_sanity()
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policy = (sanity_cfg or {}).get("policy") or {}
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drop_out_of_range = bool(policy.get("drop_out_of_range", True))
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keep_suspect = bool(policy.get("keep_suspect", True))
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for pattern, test_name in bio_patterns:
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for m in re.finditer(pattern, text):
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raw_value = (m.group(1) or "").strip()
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if not raw_value:
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continue
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# Valeurs qualitatives (troponine négative/positive/normale) :
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# pas de sanitization numérique.
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if re.fullmatch(r"[a-zA-Zéèêëàâôûùïîç]+", raw_value):
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key = (test_name, raw_value.lower())
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if key in seen:
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continue
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seen.add(key)
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counts[test_name] = counts.get(test_name, 0) + 1
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if counts[test_name] > max_per_test:
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break
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anomalie = _is_abnormal(test_name, raw_value)
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dossier.biologie_cle.append(
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BiologieCle(
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test=test_name,
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valeur=raw_value,
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valeur_num=None,
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anomalie=anomalie,
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quality="ok",
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discard_reason=None,
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)
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)
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continue
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sanitized = _sanitize_bio_value(test_name, raw_value, sanity_cfg)
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if sanitized is None:
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continue
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token, val_num, quality, reason = sanitized
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if quality == "suspect" and not keep_suspect:
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quality = "discarded"
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reason = reason or "Valeur suspecte (policy keep_suspect=false)"
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# Déduplication sur la valeur normalisée
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key = (test_name, token)
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if key in seen:
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continue
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seen.add(key)
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counts[test_name] = counts.get(test_name, 0) + 1
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if counts[test_name] > max_per_test:
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break
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if quality == "discarded":
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# On garde la trace pour audit, sans polluer les règles qualité.
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dossier.biologie_discarded.append(
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{
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"test": test_name,
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"raw": raw_value,
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"valeur": token,
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"valeur_num": val_num,
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"reason": reason,
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}
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)
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if drop_out_of_range:
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continue
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anomalie = _is_abnormal(test_name, token)
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dossier.biologie_cle.append(
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BiologieCle(
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test=test_name,
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valeur=token,
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valeur_num=val_num,
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anomalie=anomalie,
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quality=quality,
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discard_reason=reason,
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)
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)
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40
src/medical/bio_normals.py
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40
src/medical/bio_normals.py
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@@ -0,0 +1,40 @@
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"""Plages de référence biologiques et fonction d'interprétation."""
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from __future__ import annotations
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# Plages de référence biologiques (min, max) — utilisées par _is_abnormal()
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# et exportées pour le formatage du contexte LLM dans rag_search.py
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BIO_NORMALS: dict[str, tuple[float, float]] = {
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"Lipasémie": (0, 60),
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"CRP": (0, 5),
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"ASAT": (0, 40),
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"ALAT": (0, 40),
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"GGT": (0, 60),
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"PAL": (0, 150),
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"Bilirubine totale": (0, 17),
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# Ionogramme (fallback adulte ; les règles de décision utilisent reference_ranges.yaml)
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"Sodium": (135, 145),
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"Potassium": (3.5, 5.0),
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"Hémoglobine": (12, 17),
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"Plaquettes": (150, 400),
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"Leucocytes": (4, 10),
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"Créatinine": (50, 120),
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}
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def _is_abnormal(test: str, value: str) -> bool | None:
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"""Détermine si un résultat biologique est anormal."""
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try:
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val = float(value.replace(",", "."))
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except (ValueError, AttributeError):
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if value.lower() in ("négative", "negative", "normale", "normal"):
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return False
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if value.lower() in ("positive", "positif", "élevée", "elevee"):
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return True
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return None
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if test in BIO_NORMALS:
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lo, hi = BIO_NORMALS[test]
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return val > hi or val < lo
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return None
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@@ -1,26 +1,28 @@
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"""Extraction d'informations médicales structurées pour le codage CIM-10."""
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"""Extraction d'informations médicales structurées pour le codage CIM-10.
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Orchestrateur principal — délègue aux sous-modules :
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- bio_normals : constantes BIO_NORMALS, _is_abnormal()
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- bio_extraction : _extract_biologie() et helpers parsing
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- diagnostic_extraction : _extract_diagnostics(), _extract_actes(), CIM10_MAP, CCAM_MAP
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- validation_pipeline : _validate_cim10(), _validate_ccam(), _apply_*_rules()
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"""
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from __future__ import annotations
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import logging
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import re
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import unicodedata
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from datetime import datetime
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from typing import Optional
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logger = logging.getLogger(__name__)
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from .cim10_dict import lookup as dict_lookup, normalize_text, normalize_code, validate_code as cim10_validate
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from .ccam_dict import lookup as ccam_lookup, validate_code as ccam_validate
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from .das_filter import clean_diagnostic_text, is_valid_diagnostic_text, correct_known_miscodes, expand_medical_abbreviations
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from .cim10_dict import normalize_code, validate_code as cim10_validate
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from .das_filter import clean_diagnostic_text, is_valid_diagnostic_text, expand_medical_abbreviations
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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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load_lab_value_sanity,
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Imagerie,
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Sejour,
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Traitement,
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@@ -31,70 +33,31 @@ try:
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except ImportError:
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EdsnlpResult = None # type: ignore[assignment,misc]
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# Mapping diagnostics fréquents → codes CIM-10
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CIM10_MAP: dict[str, str] = {
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# Pancréatite
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"pancréatite aiguë biliaire": "K85.1",
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"pancréatite aigue biliaire": "K85.1",
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"pancréatite aiguë lithiasique": "K85.1",
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"pancréatite aigue lithiasique": "K85.1",
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"pancréatite aiguë": "K85.9",
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"pancréatite aigue": "K85.9",
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"pancréatite": "K85.9",
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# Lithiases biliaires
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"lithiase cholédoque": "K80.5",
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"lithiase du cholédoque": "K80.5",
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"calcul des canaux biliaires": "K80.5",
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"lithiase vésiculaire": "K80.2",
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"lithiases vésiculaires": "K80.2",
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"vésicule lithiasique": "K80.2",
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"colique hépatique": "K80.2",
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# Cholécystite
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"cholécystite aiguë": "K81.0",
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"cholecystite aigue": "K81.0",
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"angiocholite": "K83.0",
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# Obésité
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"obésité": "E66.0",
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"obesite": "E66.0",
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"surpoids": "E66.0",
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# Réactions médicamenteuses
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"éruption médicamenteuse": "L27.0",
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"eruption medicamenteuse": "L27.0",
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"éruption cutanée médicamenteuse": "L27.0",
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"toxidermie": "L27.0",
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"réaction au tramadol": "L27.0",
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"allergie médicamenteuse": "T88.7",
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# Douleur
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"douleur abdominale": "R10.4",
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"douleur hypochondre droit": "R10.1",
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# Ictère
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"ictère": "R17",
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"jaunisse": "R17",
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# HTA
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"hypertension artérielle": "I10",
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"hta": "I10",
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# Diabète
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"diabète type 2": "E11.9",
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"diabète de type 2": "E11.9",
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"diabète type 1": "E10.9",
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}
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# --- Imports depuis les sous-modules ---
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from .bio_extraction import _extract_biologie
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from .diagnostic_extraction import (
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_extract_diagnostics,
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_extract_actes,
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CIM10_MAP,
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CCAM_MAP,
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)
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from .validation_pipeline import (
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_validate_ccam,
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_validate_cim10,
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_apply_code_corrections,
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_apply_exclusion_rules,
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_apply_severity_rules,
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_apply_noncumul_rules,
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_remove_das_equal_dp,
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_apply_source_tracking,
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_validate_justifications,
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)
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# Mapping actes → codes CCAM
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CCAM_MAP: dict[str, str] = {
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"cholécystectomie": "HMFC004",
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"cholecystectomie": "HMFC004",
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"cholécystectomie par cœlioscopie": "HMFC004",
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"cholecystectomie par coelioscopie": "HMFC004",
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"cholangiographie": "HHHE002",
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"cholangiographie peropératoire": "HHHE002",
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"cpre": "HHHE002",
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"sphinctérotomie endoscopique": "HHHE003",
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"scanner abdominal": "ZCQK002",
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"tdm abdominal": "ZCQK002",
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"échographie abdominale": "ZCQJ001",
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"echo abdominale": "ZCQJ001",
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"irm abdominale": "ZCQN001",
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}
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# Backward compat — sera retiré dans un commit futur
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from .bio_normals import BIO_NORMALS, _is_abnormal # noqa: F401
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from .validation_pipeline import _is_dp_family_redundant # noqa: F401
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from .diagnostic_extraction import _lookup_cim10 # noqa: F401
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from .diagnostic_extraction import _DAS_PATTERNS # noqa: F401
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def extract_medical_info(
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@@ -304,239 +267,6 @@ def _extract_sejour(parsed: dict, dossier: DossierMedical) -> None:
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dossier.sejour.taille = patient["taille_cm"]
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def _extract_diagnostics(
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parsed: dict,
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text: str,
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dossier: DossierMedical,
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edsnlp_result: Optional[EdsnlpResult] = None,
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) -> None:
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"""Extrait le diagnostic principal et les diagnostics associés."""
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text_lower = text.lower()
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# Diagnostics codés depuis Trackare (prioritaires)
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for diag in parsed.get("diagnostics", []):
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texte = clean_diagnostic_text(diag.get("libelle", ""))
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texte = expand_medical_abbreviations(texte)
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is_principal = diag.get("type", "").lower() == "principal"
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# Le DP Trackare est toujours accepté (pré-codé avec CIM-10 validé).
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# Seuls les DAS passent le filtre anti-bruit.
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if not is_principal and not is_valid_diagnostic_text(texte):
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continue
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d = Diagnostic(
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texte=texte,
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cim10_suggestion=diag.get("code_cim10"),
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source="trackare",
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)
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if is_principal:
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dossier.diagnostic_principal = d
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else:
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dossier.diagnostics_associes.append(d)
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# Extraction du texte "Au total:" ou conclusion
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conclusion = ""
|
||||
m = re.search(
|
||||
r"Au total\s*[:：]?\s*(.*?)(?=\n\s*(?:Devenir|TTT|Sortie|$))",
|
||||
text,
|
||||
re.DOTALL | re.IGNORECASE,
|
||||
)
|
||||
if m:
|
||||
conclusion = m.group(1).strip()
|
||||
|
||||
# Enrichissement via edsnlp (CIM-10)
|
||||
edsnlp_codes: dict[str, str] = {}
|
||||
if edsnlp_result:
|
||||
for ent in edsnlp_result.cim10_entities:
|
||||
if not ent.negation and not ent.hypothese:
|
||||
edsnlp_codes[ent.code] = ent.texte
|
||||
|
||||
# Si pas de DP depuis le codage, chercher dans le texte
|
||||
if not dossier.diagnostic_principal:
|
||||
# D'abord essayer le fallback regex (plus précis pour les patterns spécifiques)
|
||||
dp = _find_diagnostic_principal(text_lower, conclusion)
|
||||
if dp:
|
||||
dossier.diagnostic_principal = dp
|
||||
elif edsnlp_codes:
|
||||
# Utiliser la première entité CIM-10 edsnlp comme DP
|
||||
code, texte = next(iter(edsnlp_codes.items()))
|
||||
texte_clean = texte.capitalize()
|
||||
if is_valid_diagnostic_text(texte_clean):
|
||||
dossier.diagnostic_principal = Diagnostic(
|
||||
texte=texte_clean, cim10_suggestion=code,
|
||||
source="edsnlp",
|
||||
)
|
||||
|
||||
# Diagnostics associés depuis le texte (regex)
|
||||
das = _find_diagnostics_associes(text_lower, conclusion, dossier)
|
||||
das = [d for d in das if is_valid_diagnostic_text(d.texte)]
|
||||
dossier.diagnostics_associes.extend(das)
|
||||
|
||||
# Enrichissement DAS depuis edsnlp
|
||||
if edsnlp_result:
|
||||
existing_codes = set()
|
||||
if dossier.diagnostic_principal:
|
||||
existing_codes.add(dossier.diagnostic_principal.cim10_suggestion)
|
||||
for d in dossier.diagnostics_associes:
|
||||
existing_codes.add(d.cim10_suggestion)
|
||||
|
||||
for ent in edsnlp_result.cim10_entities:
|
||||
if ent.negation or ent.hypothese:
|
||||
continue
|
||||
texte = clean_diagnostic_text(ent.texte.capitalize())
|
||||
if not is_valid_diagnostic_text(texte):
|
||||
continue
|
||||
if ent.code not in existing_codes:
|
||||
dossier.diagnostics_associes.append(Diagnostic(
|
||||
texte=texte,
|
||||
cim10_suggestion=ent.code,
|
||||
source="edsnlp",
|
||||
))
|
||||
existing_codes.add(ent.code)
|
||||
|
||||
|
||||
def _find_diagnostic_principal(text_lower: str, conclusion: str) -> Diagnostic | None:
|
||||
"""Trouve le diagnostic principal dans le texte.
|
||||
|
||||
Normalise le texte avant matching pour gérer les variations d'accents/casse.
|
||||
"""
|
||||
conclusion_norm = normalize_text(conclusion)
|
||||
|
||||
# Chercher dans la conclusion d'abord via CIM10_MAP (domain override)
|
||||
for terme, code in CIM10_MAP.items():
|
||||
if normalize_text(terme) in conclusion_norm:
|
||||
return Diagnostic(texte=terme.capitalize(), cim10_suggestion=code, source="regex")
|
||||
|
||||
text_norm = normalize_text(text_lower)
|
||||
|
||||
# Patterns courants pour le DP (normalisés, sans accents)
|
||||
dp_patterns = [
|
||||
r"pancreatite\s+aigue\s+(?:d'origine\s+)?lithiasique",
|
||||
r"pancreatite\s+aigue\s+biliaire",
|
||||
r"pancreatite\s+aigue",
|
||||
]
|
||||
for pat in dp_patterns:
|
||||
m = re.search(pat, text_norm)
|
||||
if m:
|
||||
matched = m.group(0)
|
||||
code = _lookup_cim10(matched)
|
||||
return Diagnostic(texte=matched.capitalize(), cim10_suggestion=code, source="regex")
|
||||
|
||||
return None
|
||||
|
||||
|
||||
# Patterns DAS : (pattern_normalisé, label, code_fallback)
|
||||
# Les patterns sont appliqués sur du texte normalisé (sans accents, lowercase)
|
||||
_DAS_PATTERNS: list[tuple[str, str, str]] = [
|
||||
# Lithiases biliaires
|
||||
(r"lithiase\s+(?:du\s+)?(?:bas\s+)?choledoque", "Lithiase du cholédoque", "K80.5"),
|
||||
(r"vesicule\s+lithiasique|lithiases?\s+vesiculaire", "Lithiase vésiculaire", "K80.2"),
|
||||
# Inflammation biliaire
|
||||
(r"cholecystite\s+aigue", "Cholécystite aiguë", "K81.0"),
|
||||
(r"angiocholite|cholangite", "Angiocholite", "K83.0"),
|
||||
# Réactions médicamenteuses
|
||||
(r"eruption\s+cutanee|toxidermie|reaction\s+au\s+tramadol", "Éruption cutanée médicamenteuse", "L27.0"),
|
||||
# Cardiovasculaire
|
||||
(r"hypertension\s+arterielle|\bhta\b", "Hypertension artérielle", "I10"),
|
||||
(r"fibrillation\s+auriculaire|\bfa\b(?:\s+paroxystique)?|\bacfa\b", "Fibrillation auriculaire", "I48.9"),
|
||||
(r"embolie\s+pulmonaire", "Embolie pulmonaire", "I26.9"),
|
||||
(r"thrombose\s+veineuse\s+profonde|\btvp\b", "Thrombose veineuse profonde", "I80.2"),
|
||||
# Métabolique
|
||||
(r"diabete\s+(?:sucre\s+)?(?:de\s+)?type\s+2|diabete\s+type\s*2", "Diabète de type 2", "E11.9"),
|
||||
(r"diabete\s+(?:sucre\s+)?(?:de\s+)?type\s+1|diabete\s+type\s*1", "Diabète de type 1", "E10.9"),
|
||||
(r"dyslipidemie|hypercholesterolemie", "Dyslipidémie", "E78.5"),
|
||||
(r"denutrition|malnutrition", "Dénutrition", "E46"),
|
||||
# Infectieux
|
||||
(r"pneumopathie|pneumonie", "Pneumopathie", "J18.9"),
|
||||
(r"infection\s+urinaire|pyelonephrite", "Infection urinaire", "N39.0"),
|
||||
(r"\bsepsis\b|septicemie|choc\s+septique", "Sepsis", "A41.9"),
|
||||
# Rénal
|
||||
(r"insuffisance\s+renale", "Insuffisance rénale", "N19"),
|
||||
# Hématologique
|
||||
(r"anemie", "Anémie", "D64.9"),
|
||||
# Addictions
|
||||
(r"tabagisme|tabac\s+actif", "Tabagisme", "F17.2"),
|
||||
(r"ethylisme|alcoolisme|intoxication\s+ethylique", "Éthylisme", "F10.1"),
|
||||
]
|
||||
|
||||
|
||||
def _find_diagnostics_associes(
|
||||
text_lower: str, conclusion: str, dossier: DossierMedical
|
||||
) -> list[Diagnostic]:
|
||||
"""Trouve les diagnostics associés.
|
||||
|
||||
Utilise des patterns normalisés (sans accents) pour une détection robuste.
|
||||
"""
|
||||
das: list[Diagnostic] = []
|
||||
existing_codes = set()
|
||||
if dossier.diagnostic_principal:
|
||||
existing_codes.add(dossier.diagnostic_principal.cim10_suggestion)
|
||||
for d in dossier.diagnostics_associes:
|
||||
existing_codes.add(d.cim10_suggestion)
|
||||
|
||||
text_norm = normalize_text(text_lower)
|
||||
|
||||
# Patterns DAS
|
||||
for pat, label, code in _DAS_PATTERNS:
|
||||
if re.search(pat, text_norm) and code not in existing_codes:
|
||||
das.append(Diagnostic(texte=label, cim10_suggestion=code, source="regex"))
|
||||
existing_codes.add(code)
|
||||
|
||||
# Obésité (IMC >= 30) — pattern spécial avec extraction de valeur
|
||||
m = re.search(r"imc\s*[:=]?\s*(\d{2,3}[.,]\d+)", text_norm)
|
||||
if m:
|
||||
imc_val = float(m.group(1).replace(",", "."))
|
||||
if imc_val >= 30 and "E66.0" not in existing_codes:
|
||||
das.append(Diagnostic(texte=f"Obésité (IMC {imc_val})", cim10_suggestion="E66.0", source="regex"))
|
||||
existing_codes.add("E66.0")
|
||||
|
||||
return das
|
||||
|
||||
|
||||
def _extract_actes(text: str, dossier: DossierMedical) -> None:
|
||||
"""Extrait les actes CCAM."""
|
||||
text_lower = text.lower()
|
||||
|
||||
# Cholécystectomie par cœlioscopie
|
||||
if re.search(r"chol[ée]cystectomie\s+par\s+c[oœ][ea]lioscopie", text_lower):
|
||||
date = _find_act_date(text, r"chol[ée]cystectomie")
|
||||
dossier.actes_ccam.append(ActeCCAM(
|
||||
texte="Cholécystectomie par cœlioscopie",
|
||||
code_ccam_suggestion="HMFC004",
|
||||
date=date,
|
||||
))
|
||||
elif re.search(r"chol[ée]cystectomie|cholecystectomie", text_lower):
|
||||
date = _find_act_date(text, r"chol[ée]cystectomie|cholecystectomie")
|
||||
dossier.actes_ccam.append(ActeCCAM(
|
||||
texte="Cholécystectomie",
|
||||
code_ccam_suggestion="HMFC004",
|
||||
date=date,
|
||||
))
|
||||
|
||||
# Cholangiographie
|
||||
if re.search(r"cholangiographie", text_lower):
|
||||
date = _find_act_date(text, r"cholangiographie")
|
||||
dossier.actes_ccam.append(ActeCCAM(
|
||||
texte="Cholangiographie peropératoire",
|
||||
code_ccam_suggestion="HHHE002",
|
||||
date=date,
|
||||
))
|
||||
|
||||
# TDM
|
||||
if re.search(r"(?:tdm|scanner|tomodensitométrie)", text_lower):
|
||||
date = _find_act_date(text, r"(?:TDM|scanner)")
|
||||
dossier.actes_ccam.append(ActeCCAM(
|
||||
texte="TDM abdominal",
|
||||
code_ccam_suggestion="ZCQK002",
|
||||
date=date,
|
||||
))
|
||||
|
||||
# Fallback : tenter le lookup CCAM dict pour les actes sans code
|
||||
for acte in dossier.actes_ccam:
|
||||
if not acte.code_ccam_suggestion:
|
||||
code = ccam_lookup(acte.texte, domain_overrides=CCAM_MAP)
|
||||
if code:
|
||||
acte.code_ccam_suggestion = code
|
||||
|
||||
|
||||
_ANTECEDENT_NOISE = (
|
||||
"item de", "surveillance", "température", "signes vitaux",
|
||||
"pouls", "type de note", "aucune donnée", "renseignée",
|
||||
@@ -691,183 +421,6 @@ def _match_drug_atc(med_name: str, drug_atc: dict[str, str]) -> Optional[str]:
|
||||
return None
|
||||
|
||||
|
||||
|
||||
def _norm_key(s: str) -> str:
|
||||
"""Normalise une clé (minuscules, sans accents) pour index YAML."""
|
||||
s = (s or "").strip().lower()
|
||||
s = unicodedata.normalize("NFKD", s)
|
||||
s = "".join(ch for ch in s if not unicodedata.combining(ch))
|
||||
return re.sub(r"\s+", " ", s)
|
||||
|
||||
|
||||
def _parse_float_and_token(raw: str) -> tuple[float | None, str | None]:
|
||||
"""Parse un float et renvoie aussi le token numérique normalisé (avec '.')."""
|
||||
if raw is None:
|
||||
return None, None
|
||||
s = str(raw).strip()
|
||||
m = re.search(r"(-?\d+(?:[\.,]\d+)?)", s)
|
||||
if not m:
|
||||
return None, None
|
||||
token = m.group(1).replace(",", ".")
|
||||
try:
|
||||
return float(token), token
|
||||
except ValueError:
|
||||
return None, None
|
||||
|
||||
|
||||
def _sanitize_bio_value(test_name: str, raw_value: str, sanity_cfg: dict) -> tuple[str, float, str, str | None] | None:
|
||||
"""Applique des garde-fous anti-artefacts (OCR/PDF).
|
||||
|
||||
Retour:
|
||||
(token, value_float, quality, reason) ou None si non parsable.
|
||||
quality: ok | suspect | discarded
|
||||
"""
|
||||
val, token = _parse_float_and_token(raw_value)
|
||||
if val is None or token is None:
|
||||
return None
|
||||
|
||||
key = _norm_key(test_name)
|
||||
tests_cfg = (sanity_cfg or {}).get("tests") or {}
|
||||
cfg = tests_cfg.get(key) or {}
|
||||
hard_min = cfg.get("hard_min")
|
||||
hard_max = cfg.get("hard_max")
|
||||
|
||||
if hard_min is not None and val < float(hard_min):
|
||||
return token, val, "discarded", f"Valeur hors bornes plausibles (<{hard_min})"
|
||||
if hard_max is not None and val > float(hard_max):
|
||||
return token, val, "discarded", f"Valeur hors bornes plausibles (>{hard_max})"
|
||||
|
||||
quality = "ok"
|
||||
reason: str | None = None
|
||||
|
||||
suspect_cfg = cfg.get("suspect") or {}
|
||||
single_digit_over = suspect_cfg.get("single_digit_over")
|
||||
if single_digit_over is not None:
|
||||
# Ex: potassium '8' au lieu de '4.8' (décimale perdue)
|
||||
if re.fullmatch(r"\d", str(raw_value).strip()) and val >= float(single_digit_over):
|
||||
quality = "suspect"
|
||||
reason = f"Valeur à 1 chiffre (possible décimale perdue) : vérifier dans le CR"
|
||||
|
||||
return token, val, quality, reason
|
||||
|
||||
|
||||
def _extract_biologie(text: str, dossier: DossierMedical) -> None:
|
||||
"""Extrait des résultats biologiques clés.
|
||||
|
||||
Notes:
|
||||
- Supporte des aliases (TGO/TGP, Hb, Na/K…)
|
||||
- Capte plusieurs occurrences (utile pour valider/infirmer des diagnostics)
|
||||
- Reste volontairement *simple* (regex sur texte extrait) : si une valeur est
|
||||
uniquement dans un tableau PDF mal extrait, elle peut manquer.
|
||||
"""
|
||||
# (pattern, test_name)
|
||||
bio_patterns: list[tuple[str, str]] = [
|
||||
(r"[Ll]ipas[ée]mie\s*(?:[àa=:])?\s*(\d+)\s*(?:UI/L|U/L)?", "Lipasémie"),
|
||||
(r"\bCRP\b\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:mg/[Ll])?", "CRP"),
|
||||
(r"(?:\bASAT\b|\bTGO\b)\s*[=:àa]?\s*([\d.,]+)\s*(?:N|U(?:I)?/L)?", "ASAT"),
|
||||
(r"(?:\bALAT\b|\bTGP\b)\s*[=:àa]?\s*([\d.,]+)\s*(?:N|U(?:I)?/L)?", "ALAT"),
|
||||
(r"\bGGT\b\s*[=:àa]?\s*(\d+)\s*(?:U(?:I)?/L)?", "GGT"),
|
||||
(r"\bPAL\b\s*[=:àa]?\s*(\d+)\s*(?:U(?:I)?/L)?", "PAL"),
|
||||
(r"[Bb]ilirubine\s+(?:totale\s+)?[àa=:]\s*(\d+(?:[.,]\d+)?)\s*(?:µmol/L|mg/dL)?", "Bilirubine totale"),
|
||||
|
||||
# Ionogramme / électrolytes
|
||||
(r"(?:[Ss]odium|[Nn]atr[ée]mie|(?<![A-Za-z])Na\+?(?![A-Za-z]))\s*[=:àa]?\s*([0-9]{2,3}(?:[.,][0-9]+)?)\s*(?:mmol/L|mEq/L)?", "Sodium"),
|
||||
(r"(?:[Pp]otassium|[Kk]ali[ée]mie|(?<![A-Za-z])K\+?(?![A-Za-z]))\s*[=:àa]?\s*([0-9](?:[.,][0-9]+)?)\s*(?:mmol/L|mEq/L)?", "Potassium"),
|
||||
|
||||
(r"[Tt]roponine\s+(?:us\s+)?(n[ée]gative|positive|normale)", "Troponine"),
|
||||
(r"(?:[Hh][ée]moglobine|\bHb\b)\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:g/dL|g/L)?", "Hémoglobine"),
|
||||
(r"[Pp]laquettes?\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:/mm3|G/L)?", "Plaquettes"),
|
||||
(r"[Ll]eucocytes?\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:/mm3|G/L)?", "Leucocytes"),
|
||||
(r"[Cc]r[ée]atinine?\s*[=:àa]?\s*(\d+(?:[.,]\d+)?)\s*(?:µmol/L|mg/dL)?", "Créatinine"),
|
||||
]
|
||||
|
||||
|
||||
# Anti-doublons + limite par test (évite d'exploser le JSON)
|
||||
max_per_test = 6
|
||||
counts: dict[str, int] = {}
|
||||
seen: set[tuple[str, str]] = set()
|
||||
|
||||
sanity_cfg = load_lab_value_sanity()
|
||||
policy = (sanity_cfg or {}).get("policy") or {}
|
||||
drop_out_of_range = bool(policy.get("drop_out_of_range", True))
|
||||
keep_suspect = bool(policy.get("keep_suspect", True))
|
||||
|
||||
for pattern, test_name in bio_patterns:
|
||||
for m in re.finditer(pattern, text):
|
||||
raw_value = (m.group(1) or "").strip()
|
||||
if not raw_value:
|
||||
continue
|
||||
|
||||
# Valeurs qualitatives (troponine négative/positive/normale) :
|
||||
# pas de sanitization numérique.
|
||||
if re.fullmatch(r"[a-zA-Zéèêëàâôûùïîç]+", raw_value):
|
||||
key = (test_name, raw_value.lower())
|
||||
if key in seen:
|
||||
continue
|
||||
seen.add(key)
|
||||
counts[test_name] = counts.get(test_name, 0) + 1
|
||||
if counts[test_name] > max_per_test:
|
||||
break
|
||||
anomalie = _is_abnormal(test_name, raw_value)
|
||||
dossier.biologie_cle.append(
|
||||
BiologieCle(
|
||||
test=test_name,
|
||||
valeur=raw_value,
|
||||
valeur_num=None,
|
||||
anomalie=anomalie,
|
||||
quality="ok",
|
||||
discard_reason=None,
|
||||
)
|
||||
)
|
||||
continue
|
||||
|
||||
sanitized = _sanitize_bio_value(test_name, raw_value, sanity_cfg)
|
||||
if sanitized is None:
|
||||
continue
|
||||
token, val_num, quality, reason = sanitized
|
||||
|
||||
if quality == "suspect" and not keep_suspect:
|
||||
quality = "discarded"
|
||||
reason = reason or "Valeur suspecte (policy keep_suspect=false)"
|
||||
|
||||
# Déduplication sur la valeur normalisée
|
||||
key = (test_name, token)
|
||||
if key in seen:
|
||||
continue
|
||||
seen.add(key)
|
||||
|
||||
counts[test_name] = counts.get(test_name, 0) + 1
|
||||
if counts[test_name] > max_per_test:
|
||||
break
|
||||
|
||||
if quality == "discarded":
|
||||
# On garde la trace pour audit, sans polluer les règles qualité.
|
||||
dossier.biologie_discarded.append(
|
||||
{
|
||||
"test": test_name,
|
||||
"raw": raw_value,
|
||||
"valeur": token,
|
||||
"valeur_num": val_num,
|
||||
"reason": reason,
|
||||
}
|
||||
)
|
||||
if drop_out_of_range:
|
||||
continue
|
||||
|
||||
anomalie = _is_abnormal(test_name, token)
|
||||
dossier.biologie_cle.append(
|
||||
BiologieCle(
|
||||
test=test_name,
|
||||
valeur=token,
|
||||
valeur_num=val_num,
|
||||
anomalie=anomalie,
|
||||
quality=quality,
|
||||
discard_reason=reason,
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
|
||||
def _extract_imagerie(text: str, dossier: DossierMedical) -> None:
|
||||
"""Extrait les résultats d'imagerie."""
|
||||
# TDM
|
||||
@@ -948,408 +501,3 @@ def _is_negated_by_edsnlp(term: str, negated_terms: set[str]) -> bool:
|
||||
if term_lower in neg_term or neg_term in term_lower:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _validate_ccam(dossier: DossierMedical) -> None:
|
||||
"""Valide les codes CCAM suggérés contre le dictionnaire officiel."""
|
||||
for acte in dossier.actes_ccam:
|
||||
if not acte.code_ccam_suggestion:
|
||||
acte.validite = "non_verifie"
|
||||
continue
|
||||
is_valid, desc = ccam_validate(acte.code_ccam_suggestion)
|
||||
if is_valid:
|
||||
acte.validite = "valide"
|
||||
else:
|
||||
acte.validite = "non_verifie"
|
||||
dossier.alertes_codage.append(
|
||||
f"CCAM {acte.code_ccam_suggestion} ({acte.texte}) : code absent du dictionnaire CCAM V81"
|
||||
)
|
||||
|
||||
|
||||
_INVALID_CODE_PATTERNS = {"aucun", "none", "n/a", "non_codable", "aucun_code_valide", "inconnu"}
|
||||
|
||||
|
||||
def _fallback_cim10(texte: str) -> str | None:
|
||||
"""Tente de trouver un code CIM-10 via le dictionnaire à partir du texte diagnostic."""
|
||||
code = dict_lookup(texte, domain_overrides=CIM10_MAP)
|
||||
if code:
|
||||
is_valid, _ = cim10_validate(code)
|
||||
if is_valid:
|
||||
return code
|
||||
return None
|
||||
|
||||
|
||||
def _validate_cim10(dossier: DossierMedical) -> None:
|
||||
"""Valide les codes CIM-10 suggérés par Ollama contre le dictionnaire."""
|
||||
diags: list[tuple[str, Diagnostic]] = []
|
||||
if dossier.diagnostic_principal:
|
||||
diags.append(("DP", dossier.diagnostic_principal))
|
||||
for das in dossier.diagnostics_associes:
|
||||
diags.append(("DAS", das))
|
||||
|
||||
for type_diag, diag in diags:
|
||||
if not diag.cim10_suggestion:
|
||||
continue
|
||||
|
||||
# Rejeter les hallucinations
|
||||
if diag.cim10_suggestion.lower().strip() in _INVALID_CODE_PATTERNS:
|
||||
fallback = _fallback_cim10(diag.texte)
|
||||
if fallback:
|
||||
dossier.alertes_codage.append(
|
||||
f"CIM-10 {type_diag} ({diag.texte}) : code rejeté « {diag.cim10_suggestion} » → fallback {fallback}"
|
||||
)
|
||||
diag.cim10_suggestion = fallback
|
||||
diag.cim10_confidence = "medium"
|
||||
else:
|
||||
dossier.alertes_codage.append(
|
||||
f"CIM-10 {type_diag} ({diag.texte}) : code rejeté « {diag.cim10_suggestion} »"
|
||||
)
|
||||
diag.cim10_suggestion = None
|
||||
diag.cim10_confidence = None
|
||||
continue
|
||||
|
||||
# Normaliser le format (K810 → K81.0)
|
||||
diag.cim10_suggestion = normalize_code(diag.cim10_suggestion)
|
||||
|
||||
# Valider contre le dictionnaire
|
||||
is_valid, label = cim10_validate(diag.cim10_suggestion)
|
||||
if not is_valid:
|
||||
fallback = _fallback_cim10(diag.texte)
|
||||
if fallback:
|
||||
dossier.alertes_codage.append(
|
||||
f"CIM-10 {type_diag} {diag.cim10_suggestion} ({diag.texte}) : code invalide → fallback {fallback}"
|
||||
)
|
||||
diag.cim10_suggestion = fallback
|
||||
diag.cim10_confidence = "medium"
|
||||
else:
|
||||
dossier.alertes_codage.append(
|
||||
f"CIM-10 {type_diag} {diag.cim10_suggestion} ({diag.texte}) : code absent du dictionnaire CIM-10"
|
||||
)
|
||||
diag.cim10_confidence = "low"
|
||||
|
||||
|
||||
def _find_act_date(text: str, act_pattern: str) -> str | None:
|
||||
"""Trouve la date associée à un acte."""
|
||||
# Chercher "acte le DD/MM" ou "acte le DD/MM/YYYY"
|
||||
m = re.search(
|
||||
rf"{act_pattern}.*?(?:le\s+)?(\d{{2}}/\d{{2}}(?:/\d{{4}})?)",
|
||||
text,
|
||||
re.IGNORECASE,
|
||||
)
|
||||
if m:
|
||||
return m.group(1)
|
||||
|
||||
# Chercher dans la ligne d'observation juste avant
|
||||
m = re.search(
|
||||
rf"(\d{{2}}/\d{{2}}/\d{{4}}).*?{act_pattern}",
|
||||
text,
|
||||
re.IGNORECASE,
|
||||
)
|
||||
if m:
|
||||
return m.group(1)
|
||||
return None
|
||||
|
||||
|
||||
def _apply_exclusion_rules(dossier: DossierMedical) -> None:
|
||||
"""Applique les règles d'exclusion symptôme vs diagnostic précis."""
|
||||
try:
|
||||
from .exclusion_rules import check_exclusions
|
||||
result = check_exclusions(dossier.diagnostic_principal, dossier.diagnostics_associes)
|
||||
dossier.diagnostics_associes = result.cleaned_das
|
||||
dossier.alertes_codage.extend(result.warnings)
|
||||
if result.excluded:
|
||||
logger.info(
|
||||
" Exclusions : %d DAS symptomatiques exclus",
|
||||
len(result.excluded),
|
||||
)
|
||||
except Exception:
|
||||
logger.warning("Erreur lors de l'application des règles d'exclusion", exc_info=True)
|
||||
|
||||
|
||||
def _apply_severity_rules(dossier: DossierMedical) -> None:
|
||||
"""Enrichit les diagnostics avec les informations de sévérité heuristique."""
|
||||
try:
|
||||
from .severity import enrich_dossier_severity
|
||||
alertes, _cma_count, _cms_count = enrich_dossier_severity(
|
||||
dossier.diagnostic_principal, dossier.diagnostics_associes,
|
||||
)
|
||||
dossier.alertes_codage.extend(alertes)
|
||||
except Exception:
|
||||
logger.warning("Erreur lors de l'évaluation de sévérité", exc_info=True)
|
||||
|
||||
|
||||
def _apply_code_corrections(dossier: DossierMedical) -> None:
|
||||
"""Corrige les codes CIM-10 systématiquement mal attribués par le LLM."""
|
||||
all_diags = []
|
||||
if dossier.diagnostic_principal:
|
||||
all_diags.append(dossier.diagnostic_principal)
|
||||
all_diags.extend(dossier.diagnostics_associes)
|
||||
|
||||
for diag in all_diags:
|
||||
if not diag.cim10_suggestion:
|
||||
continue
|
||||
corrected = correct_known_miscodes(diag.cim10_suggestion, diag.texte)
|
||||
if corrected:
|
||||
logger.info(" Code corrigé : %s → %s pour « %s »", diag.cim10_suggestion, corrected, diag.texte)
|
||||
diag.cim10_suggestion = corrected
|
||||
|
||||
|
||||
def _is_dp_family_redundant(das_code: str, dp_code: str) -> bool:
|
||||
"""True si le DAS est redondant avec le DP (même code, parent/enfant, ou même famille)."""
|
||||
if das_code == dp_code:
|
||||
return True
|
||||
# Relation parent/enfant → toujours redondant
|
||||
das_norm = das_code.replace(".", "")
|
||||
dp_norm = dp_code.replace(".", "")
|
||||
if das_norm.startswith(dp_norm) or dp_norm.startswith(das_norm):
|
||||
return True
|
||||
# Même famille 3 chars, sauf exceptions
|
||||
dp_family = dp_code[:3]
|
||||
if das_code[:3] == dp_family:
|
||||
# S/T (trauma) : sites différents → garder
|
||||
if dp_family[0] in ("S", "T"):
|
||||
return False
|
||||
# E10-E14 (diabète) : complications différentes → garder
|
||||
if dp_family[0] == "E" and dp_family[1:].isdigit() and 10 <= int(dp_family[1:]) <= 14:
|
||||
return False
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _remove_das_equal_dp(dossier: DossierMedical) -> None:
|
||||
"""Retire les DAS redondants avec le DP (même code, famille, ou sémantique)."""
|
||||
from .das_filter import apply_semantic_dedup
|
||||
|
||||
dp_code = dossier.diagnostic_principal.cim10_suggestion if dossier.diagnostic_principal else None
|
||||
if not dp_code:
|
||||
return
|
||||
before = len(dossier.diagnostics_associes)
|
||||
dossier.diagnostics_associes = [
|
||||
d for d in dossier.diagnostics_associes
|
||||
if not d.cim10_suggestion or not _is_dp_family_redundant(d.cim10_suggestion, dp_code)
|
||||
]
|
||||
removed = before - len(dossier.diagnostics_associes)
|
||||
if removed:
|
||||
logger.info(" DAS≈DP : %d DAS retiré(s) (famille %s du DP)", removed, dp_code[:3])
|
||||
|
||||
# Redondances sémantiques entre DAS
|
||||
dossier.diagnostics_associes = apply_semantic_dedup(dossier.diagnostics_associes)
|
||||
|
||||
|
||||
def _apply_noncumul_rules(dossier: DossierMedical) -> None:
|
||||
"""Détecte les incompatibilités de non-cumul entre actes CCAM."""
|
||||
try:
|
||||
from .ccam_noncumul import check_noncumul
|
||||
alertes = check_noncumul(dossier.actes_ccam)
|
||||
dossier.alertes_codage.extend(alertes)
|
||||
except Exception:
|
||||
logger.warning("Erreur lors de la vérification du non-cumul CCAM", exc_info=True)
|
||||
|
||||
|
||||
def _lookup_cim10(text: str) -> str | None:
|
||||
"""Cherche un code CIM-10 pour un texte donné.
|
||||
|
||||
Utilise le dictionnaire complet (10 893 codes) avec CIM10_MAP en override prioritaire.
|
||||
"""
|
||||
return dict_lookup(text, domain_overrides=CIM10_MAP)
|
||||
|
||||
|
||||
# Plages de référence biologiques (min, max) — utilisées par _is_abnormal()
|
||||
# et exportées pour le formatage du contexte LLM dans rag_search.py
|
||||
BIO_NORMALS: dict[str, tuple[float, float]] = {
|
||||
"Lipasémie": (0, 60),
|
||||
"CRP": (0, 5),
|
||||
"ASAT": (0, 40),
|
||||
"ALAT": (0, 40),
|
||||
"GGT": (0, 60),
|
||||
"PAL": (0, 150),
|
||||
"Bilirubine totale": (0, 17),
|
||||
# Ionogramme (fallback adulte ; les règles de décision utilisent reference_ranges.yaml)
|
||||
"Sodium": (135, 145),
|
||||
"Potassium": (3.5, 5.0),
|
||||
"Hémoglobine": (12, 17),
|
||||
"Plaquettes": (150, 400),
|
||||
"Leucocytes": (4, 10),
|
||||
"Créatinine": (50, 120),
|
||||
}
|
||||
|
||||
|
||||
def _is_abnormal(test: str, value: str) -> bool | None:
|
||||
"""Détermine si un résultat biologique est anormal."""
|
||||
try:
|
||||
val = float(value.replace(",", "."))
|
||||
except (ValueError, AttributeError):
|
||||
if value.lower() in ("négative", "negative", "normale", "normal"):
|
||||
return False
|
||||
if value.lower() in ("positive", "positif", "élevée", "elevee"):
|
||||
return True
|
||||
return None
|
||||
|
||||
if test in BIO_NORMALS:
|
||||
lo, hi = BIO_NORMALS[test]
|
||||
return val > hi or val < lo
|
||||
return None
|
||||
|
||||
|
||||
def _track_item(item, search_key: str, page_tracker, search_text: str) -> bool:
|
||||
"""Cherche la page source et l'extrait pour un item avec source_page/source_excerpt."""
|
||||
if item.source_page is not None:
|
||||
return False
|
||||
if not search_key:
|
||||
return False
|
||||
page = page_tracker.find_page_for_text(search_key, search_text)
|
||||
if page:
|
||||
item.source_page = page
|
||||
item.source_excerpt = page_tracker.extract_excerpt(search_key, search_text)
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _apply_source_tracking(dossier: DossierMedical, page_tracker, search_text: str) -> None:
|
||||
"""Ajoute la traçabilité source (page + extrait) à tous les éléments du dossier.
|
||||
|
||||
Cherche le texte de chaque élément dans le texte source pour retrouver
|
||||
la page d'origine et extraire un passage contextualisé.
|
||||
"""
|
||||
tracked = 0
|
||||
total = 0
|
||||
|
||||
# Diagnostics (DP + DAS)
|
||||
all_diags: list[Diagnostic] = []
|
||||
if dossier.diagnostic_principal:
|
||||
all_diags.append(dossier.diagnostic_principal)
|
||||
all_diags.extend(dossier.diagnostics_associes)
|
||||
|
||||
for diag in all_diags:
|
||||
total += 1
|
||||
if _track_item(diag, diag.texte, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Biologie
|
||||
for b in dossier.biologie_cle:
|
||||
total += 1
|
||||
search_key = f"{b.test}: {b.valeur}" if b.valeur else b.test
|
||||
if _track_item(b, search_key, page_tracker, search_text):
|
||||
tracked += 1
|
||||
elif b.valeur and _track_item(b, b.test, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Imagerie
|
||||
for img in dossier.imagerie:
|
||||
total += 1
|
||||
search_key = img.type
|
||||
if _track_item(img, search_key, page_tracker, search_text):
|
||||
tracked += 1
|
||||
elif img.conclusion and _track_item(img, img.conclusion[:50], page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Traitements
|
||||
for t in dossier.traitements_sortie:
|
||||
total += 1
|
||||
if _track_item(t, t.medicament, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Actes CCAM
|
||||
for a in dossier.actes_ccam:
|
||||
total += 1
|
||||
if _track_item(a, a.texte, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Antécédents
|
||||
for ant in dossier.antecedents:
|
||||
total += 1
|
||||
if _track_item(ant, ant.texte, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Complications
|
||||
for comp in dossier.complications:
|
||||
total += 1
|
||||
if _track_item(comp, comp.texte, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
if tracked:
|
||||
logger.info(" Traçabilité source : %d/%d éléments localisés", tracked, total)
|
||||
|
||||
|
||||
def _validate_justifications(dossier: DossierMedical) -> None:
|
||||
"""Validation croisée de tous les diagnostics via un appel LLM unique.
|
||||
|
||||
Vérifie la cohérence, les preuves cliniques et la spécificité des codes.
|
||||
Ajuste la confiance si la justification est faible et ajoute des alertes QC.
|
||||
"""
|
||||
try:
|
||||
from .ollama_client import call_ollama
|
||||
from .clinical_context import build_enriched_context, format_enriched_context
|
||||
except ImportError:
|
||||
logger.warning("Module clinical_context non disponible pour la validation QC")
|
||||
return
|
||||
|
||||
all_diags: list[tuple[str, Diagnostic]] = []
|
||||
if dossier.diagnostic_principal:
|
||||
all_diags.append(("DP", dossier.diagnostic_principal))
|
||||
for das in dossier.diagnostics_associes:
|
||||
all_diags.append(("DAS", das))
|
||||
|
||||
if not all_diags:
|
||||
return
|
||||
|
||||
# Construire le résumé des codes à valider
|
||||
codes_section = ""
|
||||
for i, (type_diag, diag) in enumerate(all_diags, 1):
|
||||
code = diag.cim10_suggestion or "?"
|
||||
justif = (diag.justification or "")[:150]
|
||||
preuves = ", ".join(p.element for p in diag.preuves_cliniques[:3]) or "aucune"
|
||||
codes_section += f"{i}. [{type_diag}] {code} — {diag.texte}\n"
|
||||
codes_section += f" Justification: {justif}\n"
|
||||
codes_section += f" Preuves: {preuves}\n\n"
|
||||
|
||||
ctx = build_enriched_context(dossier)
|
||||
ctx_str = format_enriched_context(ctx)
|
||||
|
||||
from ..prompts import QC_VALIDATION
|
||||
prompt = QC_VALIDATION.format(ctx_str=ctx_str, codes_section=codes_section)
|
||||
|
||||
try:
|
||||
result = call_ollama(prompt, temperature=0.1, max_tokens=2500, role="qc")
|
||||
except Exception:
|
||||
logger.warning("Erreur lors de l'appel Ollama pour validation QC", exc_info=True)
|
||||
return
|
||||
|
||||
if result is None:
|
||||
return
|
||||
|
||||
# Appliquer les ajustements
|
||||
validations = result.get("validations", [])
|
||||
for v in validations:
|
||||
if not isinstance(v, dict):
|
||||
continue
|
||||
num = v.get("numero")
|
||||
if not isinstance(num, int) or num < 1 or num > len(all_diags):
|
||||
continue
|
||||
type_diag, diag = all_diags[num - 1]
|
||||
conf = v.get("confidence_recommandee")
|
||||
verdict = v.get("verdict")
|
||||
commentaire = v.get("commentaire", "")
|
||||
|
||||
if conf in ("high", "medium", "low") and conf != diag.cim10_confidence:
|
||||
old = diag.cim10_confidence
|
||||
diag.cim10_confidence = conf
|
||||
if old and conf != old:
|
||||
dossier.alertes_codage.append(
|
||||
f"QC: {type_diag} {diag.cim10_suggestion} confiance {old}\u2192{conf} \u2014 {commentaire}"
|
||||
)
|
||||
|
||||
if verdict == "supprimer" and type_diag == "DAS":
|
||||
dossier.alertes_codage.append(
|
||||
f"QC: DAS {diag.cim10_suggestion} ({diag.texte}) à reconsidérer \u2014 {commentaire}"
|
||||
)
|
||||
|
||||
alertes_globales = result.get("alertes_globales", [])
|
||||
if isinstance(alertes_globales, str):
|
||||
alertes_globales = [alertes_globales]
|
||||
for a in alertes_globales:
|
||||
if isinstance(a, str) and a.strip():
|
||||
dossier.alertes_codage.append(f"QC: {a}")
|
||||
|
||||
logger.info(" QC batch : %d validations, %d alertes globales",
|
||||
len(validations), len(alertes_globales))
|
||||
|
||||
@@ -7,7 +7,7 @@ cliniques structurées pour améliorer la qualité du codage CIM-10.
|
||||
from __future__ import annotations
|
||||
|
||||
from ..config import DossierMedical
|
||||
from .cim10_extractor import BIO_NORMALS
|
||||
from .bio_normals import BIO_NORMALS
|
||||
|
||||
# Seuils d'interprétation biologique (test → liste de (seuil, direction, interprétation))
|
||||
# Ordre décroissant : le premier seuil franchi donne l'interprétation
|
||||
|
||||
347
src/medical/diagnostic_extraction.py
Normal file
347
src/medical/diagnostic_extraction.py
Normal file
@@ -0,0 +1,347 @@
|
||||
"""Extraction des diagnostics (DP, DAS) et actes CCAM depuis le texte médical."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import re
|
||||
from typing import Optional
|
||||
|
||||
from .cim10_dict import lookup as dict_lookup, normalize_text, validate_code as cim10_validate
|
||||
from .ccam_dict import lookup as ccam_lookup, validate_code as ccam_validate
|
||||
from .das_filter import clean_diagnostic_text, is_valid_diagnostic_text, expand_medical_abbreviations
|
||||
from ..config import ActeCCAM, Diagnostic, DossierMedical
|
||||
|
||||
try:
|
||||
from .edsnlp_pipeline import EdsnlpResult
|
||||
except ImportError:
|
||||
EdsnlpResult = None # type: ignore[assignment,misc]
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Mapping diagnostics fréquents → codes CIM-10
|
||||
CIM10_MAP: dict[str, str] = {
|
||||
# Pancréatite
|
||||
"pancréatite aiguë biliaire": "K85.1",
|
||||
"pancréatite aigue biliaire": "K85.1",
|
||||
"pancréatite aiguë lithiasique": "K85.1",
|
||||
"pancréatite aigue lithiasique": "K85.1",
|
||||
"pancréatite aiguë": "K85.9",
|
||||
"pancréatite aigue": "K85.9",
|
||||
"pancréatite": "K85.9",
|
||||
# Lithiases biliaires
|
||||
"lithiase cholédoque": "K80.5",
|
||||
"lithiase du cholédoque": "K80.5",
|
||||
"calcul des canaux biliaires": "K80.5",
|
||||
"lithiase vésiculaire": "K80.2",
|
||||
"lithiases vésiculaires": "K80.2",
|
||||
"vésicule lithiasique": "K80.2",
|
||||
"colique hépatique": "K80.2",
|
||||
# Cholécystite
|
||||
"cholécystite aiguë": "K81.0",
|
||||
"cholecystite aigue": "K81.0",
|
||||
"angiocholite": "K83.0",
|
||||
# Obésité
|
||||
"obésité": "E66.0",
|
||||
"obesite": "E66.0",
|
||||
"surpoids": "E66.0",
|
||||
# Réactions médicamenteuses
|
||||
"éruption médicamenteuse": "L27.0",
|
||||
"eruption medicamenteuse": "L27.0",
|
||||
"éruption cutanée médicamenteuse": "L27.0",
|
||||
"toxidermie": "L27.0",
|
||||
"réaction au tramadol": "L27.0",
|
||||
"allergie médicamenteuse": "T88.7",
|
||||
# Douleur
|
||||
"douleur abdominale": "R10.4",
|
||||
"douleur hypochondre droit": "R10.1",
|
||||
# Ictère
|
||||
"ictère": "R17",
|
||||
"jaunisse": "R17",
|
||||
# HTA
|
||||
"hypertension artérielle": "I10",
|
||||
"hta": "I10",
|
||||
# Diabète
|
||||
"diabète type 2": "E11.9",
|
||||
"diabète de type 2": "E11.9",
|
||||
"diabète type 1": "E10.9",
|
||||
}
|
||||
|
||||
# Mapping actes → codes CCAM
|
||||
CCAM_MAP: dict[str, str] = {
|
||||
"cholécystectomie": "HMFC004",
|
||||
"cholecystectomie": "HMFC004",
|
||||
"cholécystectomie par cœlioscopie": "HMFC004",
|
||||
"cholecystectomie par coelioscopie": "HMFC004",
|
||||
"cholangiographie": "HHHE002",
|
||||
"cholangiographie peropératoire": "HHHE002",
|
||||
"cpre": "HHHE002",
|
||||
"sphinctérotomie endoscopique": "HHHE003",
|
||||
"scanner abdominal": "ZCQK002",
|
||||
"tdm abdominal": "ZCQK002",
|
||||
"échographie abdominale": "ZCQJ001",
|
||||
"echo abdominale": "ZCQJ001",
|
||||
"irm abdominale": "ZCQN001",
|
||||
}
|
||||
|
||||
|
||||
# Patterns DAS : (pattern_normalisé, label, code_fallback)
|
||||
# Les patterns sont appliqués sur du texte normalisé (sans accents, lowercase)
|
||||
_DAS_PATTERNS: list[tuple[str, str, str]] = [
|
||||
# Lithiases biliaires
|
||||
(r"lithiase\s+(?:du\s+)?(?:bas\s+)?choledoque", "Lithiase du cholédoque", "K80.5"),
|
||||
(r"vesicule\s+lithiasique|lithiases?\s+vesiculaire", "Lithiase vésiculaire", "K80.2"),
|
||||
# Inflammation biliaire
|
||||
(r"cholecystite\s+aigue", "Cholécystite aiguë", "K81.0"),
|
||||
(r"angiocholite|cholangite", "Angiocholite", "K83.0"),
|
||||
# Réactions médicamenteuses
|
||||
(r"eruption\s+cutanee|toxidermie|reaction\s+au\s+tramadol", "Éruption cutanée médicamenteuse", "L27.0"),
|
||||
# Cardiovasculaire
|
||||
(r"hypertension\s+arterielle|\bhta\b", "Hypertension artérielle", "I10"),
|
||||
(r"fibrillation\s+auriculaire|\bfa\b(?:\s+paroxystique)?|\bacfa\b", "Fibrillation auriculaire", "I48.9"),
|
||||
(r"embolie\s+pulmonaire", "Embolie pulmonaire", "I26.9"),
|
||||
(r"thrombose\s+veineuse\s+profonde|\btvp\b", "Thrombose veineuse profonde", "I80.2"),
|
||||
# Métabolique
|
||||
(r"diabete\s+(?:sucre\s+)?(?:de\s+)?type\s+2|diabete\s+type\s*2", "Diabète de type 2", "E11.9"),
|
||||
(r"diabete\s+(?:sucre\s+)?(?:de\s+)?type\s+1|diabete\s+type\s*1", "Diabète de type 1", "E10.9"),
|
||||
(r"dyslipidemie|hypercholesterolemie", "Dyslipidémie", "E78.5"),
|
||||
(r"denutrition|malnutrition", "Dénutrition", "E46"),
|
||||
# Infectieux
|
||||
(r"pneumopathie|pneumonie", "Pneumopathie", "J18.9"),
|
||||
(r"infection\s+urinaire|pyelonephrite", "Infection urinaire", "N39.0"),
|
||||
(r"\bsepsis\b|septicemie|choc\s+septique", "Sepsis", "A41.9"),
|
||||
# Rénal
|
||||
(r"insuffisance\s+renale", "Insuffisance rénale", "N19"),
|
||||
# Hématologique
|
||||
(r"anemie", "Anémie", "D64.9"),
|
||||
# Addictions
|
||||
(r"tabagisme|tabac\s+actif", "Tabagisme", "F17.2"),
|
||||
(r"ethylisme|alcoolisme|intoxication\s+ethylique", "Éthylisme", "F10.1"),
|
||||
]
|
||||
|
||||
|
||||
def _extract_diagnostics(
|
||||
parsed: dict,
|
||||
text: str,
|
||||
dossier: DossierMedical,
|
||||
edsnlp_result: Optional[EdsnlpResult] = None,
|
||||
) -> None:
|
||||
"""Extrait le diagnostic principal et les diagnostics associés."""
|
||||
text_lower = text.lower()
|
||||
|
||||
# Diagnostics codés depuis Trackare (prioritaires)
|
||||
for diag in parsed.get("diagnostics", []):
|
||||
texte = clean_diagnostic_text(diag.get("libelle", ""))
|
||||
texte = expand_medical_abbreviations(texte)
|
||||
is_principal = diag.get("type", "").lower() == "principal"
|
||||
# Le DP Trackare est toujours accepté (pré-codé avec CIM-10 validé).
|
||||
# Seuls les DAS passent le filtre anti-bruit.
|
||||
if not is_principal and not is_valid_diagnostic_text(texte):
|
||||
continue
|
||||
d = Diagnostic(
|
||||
texte=texte,
|
||||
cim10_suggestion=diag.get("code_cim10"),
|
||||
source="trackare",
|
||||
)
|
||||
if is_principal:
|
||||
dossier.diagnostic_principal = d
|
||||
else:
|
||||
dossier.diagnostics_associes.append(d)
|
||||
|
||||
# Extraction du texte "Au total:" ou conclusion
|
||||
conclusion = ""
|
||||
m = re.search(
|
||||
r"Au total\s*[:：]?\s*(.*?)(?=\n\s*(?:Devenir|TTT|Sortie|$))",
|
||||
text,
|
||||
re.DOTALL | re.IGNORECASE,
|
||||
)
|
||||
if m:
|
||||
conclusion = m.group(1).strip()
|
||||
|
||||
# Enrichissement via edsnlp (CIM-10)
|
||||
edsnlp_codes: dict[str, str] = {}
|
||||
if edsnlp_result:
|
||||
for ent in edsnlp_result.cim10_entities:
|
||||
if not ent.negation and not ent.hypothese:
|
||||
edsnlp_codes[ent.code] = ent.texte
|
||||
|
||||
# Si pas de DP depuis le codage, chercher dans le texte
|
||||
if not dossier.diagnostic_principal:
|
||||
# D'abord essayer le fallback regex (plus précis pour les patterns spécifiques)
|
||||
dp = _find_diagnostic_principal(text_lower, conclusion)
|
||||
if dp:
|
||||
dossier.diagnostic_principal = dp
|
||||
elif edsnlp_codes:
|
||||
# Utiliser la première entité CIM-10 edsnlp comme DP
|
||||
code, texte = next(iter(edsnlp_codes.items()))
|
||||
texte_clean = texte.capitalize()
|
||||
if is_valid_diagnostic_text(texte_clean):
|
||||
dossier.diagnostic_principal = Diagnostic(
|
||||
texte=texte_clean, cim10_suggestion=code,
|
||||
source="edsnlp",
|
||||
)
|
||||
|
||||
# Diagnostics associés depuis le texte (regex)
|
||||
das = _find_diagnostics_associes(text_lower, conclusion, dossier)
|
||||
das = [d for d in das if is_valid_diagnostic_text(d.texte)]
|
||||
dossier.diagnostics_associes.extend(das)
|
||||
|
||||
# Enrichissement DAS depuis edsnlp
|
||||
if edsnlp_result:
|
||||
existing_codes = set()
|
||||
if dossier.diagnostic_principal:
|
||||
existing_codes.add(dossier.diagnostic_principal.cim10_suggestion)
|
||||
for d in dossier.diagnostics_associes:
|
||||
existing_codes.add(d.cim10_suggestion)
|
||||
|
||||
for ent in edsnlp_result.cim10_entities:
|
||||
if ent.negation or ent.hypothese:
|
||||
continue
|
||||
texte = clean_diagnostic_text(ent.texte.capitalize())
|
||||
if not is_valid_diagnostic_text(texte):
|
||||
continue
|
||||
if ent.code not in existing_codes:
|
||||
dossier.diagnostics_associes.append(Diagnostic(
|
||||
texte=texte,
|
||||
cim10_suggestion=ent.code,
|
||||
source="edsnlp",
|
||||
))
|
||||
existing_codes.add(ent.code)
|
||||
|
||||
|
||||
def _find_diagnostic_principal(text_lower: str, conclusion: str) -> Diagnostic | None:
|
||||
"""Trouve le diagnostic principal dans le texte.
|
||||
|
||||
Normalise le texte avant matching pour gérer les variations d'accents/casse.
|
||||
"""
|
||||
conclusion_norm = normalize_text(conclusion)
|
||||
|
||||
# Chercher dans la conclusion d'abord via CIM10_MAP (domain override)
|
||||
for terme, code in CIM10_MAP.items():
|
||||
if normalize_text(terme) in conclusion_norm:
|
||||
return Diagnostic(texte=terme.capitalize(), cim10_suggestion=code, source="regex")
|
||||
|
||||
text_norm = normalize_text(text_lower)
|
||||
|
||||
# Patterns courants pour le DP (normalisés, sans accents)
|
||||
dp_patterns = [
|
||||
r"pancreatite\s+aigue\s+(?:d'origine\s+)?lithiasique",
|
||||
r"pancreatite\s+aigue\s+biliaire",
|
||||
r"pancreatite\s+aigue",
|
||||
]
|
||||
for pat in dp_patterns:
|
||||
m = re.search(pat, text_norm)
|
||||
if m:
|
||||
matched = m.group(0)
|
||||
code = _lookup_cim10(matched)
|
||||
return Diagnostic(texte=matched.capitalize(), cim10_suggestion=code, source="regex")
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def _find_diagnostics_associes(
|
||||
text_lower: str, conclusion: str, dossier: DossierMedical
|
||||
) -> list[Diagnostic]:
|
||||
"""Trouve les diagnostics associés.
|
||||
|
||||
Utilise des patterns normalisés (sans accents) pour une détection robuste.
|
||||
"""
|
||||
das: list[Diagnostic] = []
|
||||
existing_codes = set()
|
||||
if dossier.diagnostic_principal:
|
||||
existing_codes.add(dossier.diagnostic_principal.cim10_suggestion)
|
||||
for d in dossier.diagnostics_associes:
|
||||
existing_codes.add(d.cim10_suggestion)
|
||||
|
||||
text_norm = normalize_text(text_lower)
|
||||
|
||||
# Patterns DAS
|
||||
for pat, label, code in _DAS_PATTERNS:
|
||||
if re.search(pat, text_norm) and code not in existing_codes:
|
||||
das.append(Diagnostic(texte=label, cim10_suggestion=code, source="regex"))
|
||||
existing_codes.add(code)
|
||||
|
||||
# Obésité (IMC >= 30) — pattern spécial avec extraction de valeur
|
||||
m = re.search(r"imc\s*[:=]?\s*(\d{2,3}[.,]\d+)", text_norm)
|
||||
if m:
|
||||
imc_val = float(m.group(1).replace(",", "."))
|
||||
if imc_val >= 30 and "E66.0" not in existing_codes:
|
||||
das.append(Diagnostic(texte=f"Obésité (IMC {imc_val})", cim10_suggestion="E66.0", source="regex"))
|
||||
existing_codes.add("E66.0")
|
||||
|
||||
return das
|
||||
|
||||
|
||||
def _extract_actes(text: str, dossier: DossierMedical) -> None:
|
||||
"""Extrait les actes CCAM."""
|
||||
text_lower = text.lower()
|
||||
|
||||
# Cholécystectomie par cœlioscopie
|
||||
if re.search(r"chol[ée]cystectomie\s+par\s+c[oœ][ea]lioscopie", text_lower):
|
||||
date = _find_act_date(text, r"chol[ée]cystectomie")
|
||||
dossier.actes_ccam.append(ActeCCAM(
|
||||
texte="Cholécystectomie par cœlioscopie",
|
||||
code_ccam_suggestion="HMFC004",
|
||||
date=date,
|
||||
))
|
||||
elif re.search(r"chol[ée]cystectomie|cholecystectomie", text_lower):
|
||||
date = _find_act_date(text, r"chol[ée]cystectomie|cholecystectomie")
|
||||
dossier.actes_ccam.append(ActeCCAM(
|
||||
texte="Cholécystectomie",
|
||||
code_ccam_suggestion="HMFC004",
|
||||
date=date,
|
||||
))
|
||||
|
||||
# Cholangiographie
|
||||
if re.search(r"cholangiographie", text_lower):
|
||||
date = _find_act_date(text, r"cholangiographie")
|
||||
dossier.actes_ccam.append(ActeCCAM(
|
||||
texte="Cholangiographie peropératoire",
|
||||
code_ccam_suggestion="HHHE002",
|
||||
date=date,
|
||||
))
|
||||
|
||||
# TDM
|
||||
if re.search(r"(?:tdm|scanner|tomodensitométrie)", text_lower):
|
||||
date = _find_act_date(text, r"(?:TDM|scanner)")
|
||||
dossier.actes_ccam.append(ActeCCAM(
|
||||
texte="TDM abdominal",
|
||||
code_ccam_suggestion="ZCQK002",
|
||||
date=date,
|
||||
))
|
||||
|
||||
# Fallback : tenter le lookup CCAM dict pour les actes sans code
|
||||
for acte in dossier.actes_ccam:
|
||||
if not acte.code_ccam_suggestion:
|
||||
code = ccam_lookup(acte.texte, domain_overrides=CCAM_MAP)
|
||||
if code:
|
||||
acte.code_ccam_suggestion = code
|
||||
|
||||
|
||||
def _find_act_date(text: str, act_pattern: str) -> str | None:
|
||||
"""Trouve la date associée à un acte."""
|
||||
# Chercher "acte le DD/MM" ou "acte le DD/MM/YYYY"
|
||||
m = re.search(
|
||||
rf"{act_pattern}.*?(?:le\s+)?(\d{{2}}/\d{{2}}(?:/\d{{4}})?)",
|
||||
text,
|
||||
re.IGNORECASE,
|
||||
)
|
||||
if m:
|
||||
return m.group(1)
|
||||
|
||||
# Chercher dans la ligne d'observation juste avant
|
||||
m = re.search(
|
||||
rf"(\d{{2}}/\d{{2}}/\d{{4}}).*?{act_pattern}",
|
||||
text,
|
||||
re.IGNORECASE,
|
||||
)
|
||||
if m:
|
||||
return m.group(1)
|
||||
return None
|
||||
|
||||
|
||||
def _lookup_cim10(text: str) -> str | None:
|
||||
"""Cherche un code CIM-10 pour un texte donné.
|
||||
|
||||
Utilise le dictionnaire complet (10 893 codes) avec CIM10_MAP en override prioritaire.
|
||||
"""
|
||||
return dict_lookup(text, domain_overrides=CIM10_MAP)
|
||||
@@ -20,7 +20,7 @@ from ..config import (
|
||||
Traitement,
|
||||
)
|
||||
from ..medical.das_filter import is_valid_diagnostic_text, apply_semantic_dedup
|
||||
from ..medical.cim10_extractor import _is_dp_family_redundant
|
||||
from ..medical.validation_pipeline import _is_dp_family_redundant
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@@ -12,7 +12,7 @@ from ..config import (
|
||||
EMBEDDING_MODEL, RERANKER_MODEL,
|
||||
)
|
||||
from .cim10_dict import normalize_code, validate_code as cim10_validate, fallback_parent_code
|
||||
from .cim10_extractor import BIO_NORMALS
|
||||
from .bio_normals import BIO_NORMALS
|
||||
from .clinical_context import build_enriched_context, format_enriched_context
|
||||
from .ccam_dict import validate_code as ccam_validate
|
||||
from .ollama_client import call_ollama, parse_json_response
|
||||
|
||||
349
src/medical/validation_pipeline.py
Normal file
349
src/medical/validation_pipeline.py
Normal file
@@ -0,0 +1,349 @@
|
||||
"""Pipeline de validation et post-traitement des codes CIM-10 et CCAM."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
|
||||
from .cim10_dict import lookup as dict_lookup, normalize_code, validate_code as cim10_validate
|
||||
from .ccam_dict import validate_code as ccam_validate
|
||||
from .das_filter import correct_known_miscodes, apply_semantic_dedup
|
||||
from ..config import Diagnostic, DossierMedical
|
||||
from .diagnostic_extraction import CIM10_MAP
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
_INVALID_CODE_PATTERNS = {"aucun", "none", "n/a", "non_codable", "aucun_code_valide", "inconnu"}
|
||||
|
||||
|
||||
def _fallback_cim10(texte: str) -> str | None:
|
||||
"""Tente de trouver un code CIM-10 via le dictionnaire à partir du texte diagnostic."""
|
||||
code = dict_lookup(texte, domain_overrides=CIM10_MAP)
|
||||
if code:
|
||||
is_valid, _ = cim10_validate(code)
|
||||
if is_valid:
|
||||
return code
|
||||
return None
|
||||
|
||||
|
||||
def _validate_ccam(dossier: DossierMedical) -> None:
|
||||
"""Valide les codes CCAM suggérés contre le dictionnaire officiel."""
|
||||
for acte in dossier.actes_ccam:
|
||||
if not acte.code_ccam_suggestion:
|
||||
acte.validite = "non_verifie"
|
||||
continue
|
||||
is_valid, desc = ccam_validate(acte.code_ccam_suggestion)
|
||||
if is_valid:
|
||||
acte.validite = "valide"
|
||||
else:
|
||||
acte.validite = "non_verifie"
|
||||
dossier.alertes_codage.append(
|
||||
f"CCAM {acte.code_ccam_suggestion} ({acte.texte}) : code absent du dictionnaire CCAM V81"
|
||||
)
|
||||
|
||||
|
||||
def _validate_cim10(dossier: DossierMedical) -> None:
|
||||
"""Valide les codes CIM-10 suggérés par Ollama contre le dictionnaire."""
|
||||
diags: list[tuple[str, Diagnostic]] = []
|
||||
if dossier.diagnostic_principal:
|
||||
diags.append(("DP", dossier.diagnostic_principal))
|
||||
for das in dossier.diagnostics_associes:
|
||||
diags.append(("DAS", das))
|
||||
|
||||
for type_diag, diag in diags:
|
||||
if not diag.cim10_suggestion:
|
||||
continue
|
||||
|
||||
# Rejeter les hallucinations
|
||||
if diag.cim10_suggestion.lower().strip() in _INVALID_CODE_PATTERNS:
|
||||
fallback = _fallback_cim10(diag.texte)
|
||||
if fallback:
|
||||
dossier.alertes_codage.append(
|
||||
f"CIM-10 {type_diag} ({diag.texte}) : code rejeté « {diag.cim10_suggestion} » → fallback {fallback}"
|
||||
)
|
||||
diag.cim10_suggestion = fallback
|
||||
diag.cim10_confidence = "medium"
|
||||
else:
|
||||
dossier.alertes_codage.append(
|
||||
f"CIM-10 {type_diag} ({diag.texte}) : code rejeté « {diag.cim10_suggestion} »"
|
||||
)
|
||||
diag.cim10_suggestion = None
|
||||
diag.cim10_confidence = None
|
||||
continue
|
||||
|
||||
# Normaliser le format (K810 → K81.0)
|
||||
diag.cim10_suggestion = normalize_code(diag.cim10_suggestion)
|
||||
|
||||
# Valider contre le dictionnaire
|
||||
is_valid, label = cim10_validate(diag.cim10_suggestion)
|
||||
if not is_valid:
|
||||
fallback = _fallback_cim10(diag.texte)
|
||||
if fallback:
|
||||
dossier.alertes_codage.append(
|
||||
f"CIM-10 {type_diag} {diag.cim10_suggestion} ({diag.texte}) : code invalide → fallback {fallback}"
|
||||
)
|
||||
diag.cim10_suggestion = fallback
|
||||
diag.cim10_confidence = "medium"
|
||||
else:
|
||||
dossier.alertes_codage.append(
|
||||
f"CIM-10 {type_diag} {diag.cim10_suggestion} ({diag.texte}) : code absent du dictionnaire CIM-10"
|
||||
)
|
||||
diag.cim10_confidence = "low"
|
||||
|
||||
|
||||
def _apply_code_corrections(dossier: DossierMedical) -> None:
|
||||
"""Corrige les codes CIM-10 systématiquement mal attribués par le LLM."""
|
||||
all_diags = []
|
||||
if dossier.diagnostic_principal:
|
||||
all_diags.append(dossier.diagnostic_principal)
|
||||
all_diags.extend(dossier.diagnostics_associes)
|
||||
|
||||
for diag in all_diags:
|
||||
if not diag.cim10_suggestion:
|
||||
continue
|
||||
corrected = correct_known_miscodes(diag.cim10_suggestion, diag.texte)
|
||||
if corrected:
|
||||
logger.info(" Code corrigé : %s → %s pour « %s »", diag.cim10_suggestion, corrected, diag.texte)
|
||||
diag.cim10_suggestion = corrected
|
||||
|
||||
|
||||
def _apply_exclusion_rules(dossier: DossierMedical) -> None:
|
||||
"""Applique les règles d'exclusion symptôme vs diagnostic précis."""
|
||||
try:
|
||||
from .exclusion_rules import check_exclusions
|
||||
result = check_exclusions(dossier.diagnostic_principal, dossier.diagnostics_associes)
|
||||
dossier.diagnostics_associes = result.cleaned_das
|
||||
dossier.alertes_codage.extend(result.warnings)
|
||||
if result.excluded:
|
||||
logger.info(
|
||||
" Exclusions : %d DAS symptomatiques exclus",
|
||||
len(result.excluded),
|
||||
)
|
||||
except Exception:
|
||||
logger.warning("Erreur lors de l'application des règles d'exclusion", exc_info=True)
|
||||
|
||||
|
||||
def _apply_severity_rules(dossier: DossierMedical) -> None:
|
||||
"""Enrichit les diagnostics avec les informations de sévérité heuristique."""
|
||||
try:
|
||||
from .severity import enrich_dossier_severity
|
||||
alertes, _cma_count, _cms_count = enrich_dossier_severity(
|
||||
dossier.diagnostic_principal, dossier.diagnostics_associes,
|
||||
)
|
||||
dossier.alertes_codage.extend(alertes)
|
||||
except Exception:
|
||||
logger.warning("Erreur lors de l'évaluation de sévérité", exc_info=True)
|
||||
|
||||
|
||||
def _apply_noncumul_rules(dossier: DossierMedical) -> None:
|
||||
"""Détecte les incompatibilités de non-cumul entre actes CCAM."""
|
||||
try:
|
||||
from .ccam_noncumul import check_noncumul
|
||||
alertes = check_noncumul(dossier.actes_ccam)
|
||||
dossier.alertes_codage.extend(alertes)
|
||||
except Exception:
|
||||
logger.warning("Erreur lors de la vérification du non-cumul CCAM", exc_info=True)
|
||||
|
||||
|
||||
def _is_dp_family_redundant(das_code: str, dp_code: str) -> bool:
|
||||
"""True si le DAS est redondant avec le DP (même code, parent/enfant, ou même famille)."""
|
||||
if das_code == dp_code:
|
||||
return True
|
||||
# Relation parent/enfant → toujours redondant
|
||||
das_norm = das_code.replace(".", "")
|
||||
dp_norm = dp_code.replace(".", "")
|
||||
if das_norm.startswith(dp_norm) or dp_norm.startswith(das_norm):
|
||||
return True
|
||||
# Même famille 3 chars, sauf exceptions
|
||||
dp_family = dp_code[:3]
|
||||
if das_code[:3] == dp_family:
|
||||
# S/T (trauma) : sites différents → garder
|
||||
if dp_family[0] in ("S", "T"):
|
||||
return False
|
||||
# E10-E14 (diabète) : complications différentes → garder
|
||||
if dp_family[0] == "E" and dp_family[1:].isdigit() and 10 <= int(dp_family[1:]) <= 14:
|
||||
return False
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _remove_das_equal_dp(dossier: DossierMedical) -> None:
|
||||
"""Retire les DAS redondants avec le DP (même code, famille, ou sémantique)."""
|
||||
dp_code = dossier.diagnostic_principal.cim10_suggestion if dossier.diagnostic_principal else None
|
||||
if not dp_code:
|
||||
return
|
||||
before = len(dossier.diagnostics_associes)
|
||||
dossier.diagnostics_associes = [
|
||||
d for d in dossier.diagnostics_associes
|
||||
if not d.cim10_suggestion or not _is_dp_family_redundant(d.cim10_suggestion, dp_code)
|
||||
]
|
||||
removed = before - len(dossier.diagnostics_associes)
|
||||
if removed:
|
||||
logger.info(" DAS≈DP : %d DAS retiré(s) (famille %s du DP)", removed, dp_code[:3])
|
||||
|
||||
# Redondances sémantiques entre DAS
|
||||
dossier.diagnostics_associes = apply_semantic_dedup(dossier.diagnostics_associes)
|
||||
|
||||
|
||||
def _track_item(item, search_key: str, page_tracker, search_text: str) -> bool:
|
||||
"""Cherche la page source et l'extrait pour un item avec source_page/source_excerpt."""
|
||||
if item.source_page is not None:
|
||||
return False
|
||||
if not search_key:
|
||||
return False
|
||||
page = page_tracker.find_page_for_text(search_key, search_text)
|
||||
if page:
|
||||
item.source_page = page
|
||||
item.source_excerpt = page_tracker.extract_excerpt(search_key, search_text)
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _apply_source_tracking(dossier: DossierMedical, page_tracker, search_text: str) -> None:
|
||||
"""Ajoute la traçabilité source (page + extrait) à tous les éléments du dossier.
|
||||
|
||||
Cherche le texte de chaque élément dans le texte source pour retrouver
|
||||
la page d'origine et extraire un passage contextualisé.
|
||||
"""
|
||||
tracked = 0
|
||||
total = 0
|
||||
|
||||
# Diagnostics (DP + DAS)
|
||||
all_diags: list[Diagnostic] = []
|
||||
if dossier.diagnostic_principal:
|
||||
all_diags.append(dossier.diagnostic_principal)
|
||||
all_diags.extend(dossier.diagnostics_associes)
|
||||
|
||||
for diag in all_diags:
|
||||
total += 1
|
||||
if _track_item(diag, diag.texte, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Biologie
|
||||
for b in dossier.biologie_cle:
|
||||
total += 1
|
||||
search_key = f"{b.test}: {b.valeur}" if b.valeur else b.test
|
||||
if _track_item(b, search_key, page_tracker, search_text):
|
||||
tracked += 1
|
||||
elif b.valeur and _track_item(b, b.test, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Imagerie
|
||||
for img in dossier.imagerie:
|
||||
total += 1
|
||||
search_key = img.type
|
||||
if _track_item(img, search_key, page_tracker, search_text):
|
||||
tracked += 1
|
||||
elif img.conclusion and _track_item(img, img.conclusion[:50], page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Traitements
|
||||
for t in dossier.traitements_sortie:
|
||||
total += 1
|
||||
if _track_item(t, t.medicament, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Actes CCAM
|
||||
for a in dossier.actes_ccam:
|
||||
total += 1
|
||||
if _track_item(a, a.texte, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Antécédents
|
||||
for ant in dossier.antecedents:
|
||||
total += 1
|
||||
if _track_item(ant, ant.texte, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
# Complications
|
||||
for comp in dossier.complications:
|
||||
total += 1
|
||||
if _track_item(comp, comp.texte, page_tracker, search_text):
|
||||
tracked += 1
|
||||
|
||||
if tracked:
|
||||
logger.info(" Traçabilité source : %d/%d éléments localisés", tracked, total)
|
||||
|
||||
|
||||
def _validate_justifications(dossier: DossierMedical) -> None:
|
||||
"""Validation croisée de tous les diagnostics via un appel LLM unique.
|
||||
|
||||
Vérifie la cohérence, les preuves cliniques et la spécificité des codes.
|
||||
Ajuste la confiance si la justification est faible et ajoute des alertes QC.
|
||||
"""
|
||||
try:
|
||||
from .ollama_client import call_ollama
|
||||
from .clinical_context import build_enriched_context, format_enriched_context
|
||||
except ImportError:
|
||||
logger.warning("Module clinical_context non disponible pour la validation QC")
|
||||
return
|
||||
|
||||
all_diags: list[tuple[str, Diagnostic]] = []
|
||||
if dossier.diagnostic_principal:
|
||||
all_diags.append(("DP", dossier.diagnostic_principal))
|
||||
for das in dossier.diagnostics_associes:
|
||||
all_diags.append(("DAS", das))
|
||||
|
||||
if not all_diags:
|
||||
return
|
||||
|
||||
# Construire le résumé des codes à valider
|
||||
codes_section = ""
|
||||
for i, (type_diag, diag) in enumerate(all_diags, 1):
|
||||
code = diag.cim10_suggestion or "?"
|
||||
justif = (diag.justification or "")[:150]
|
||||
preuves = ", ".join(p.element for p in diag.preuves_cliniques[:3]) or "aucune"
|
||||
codes_section += f"{i}. [{type_diag}] {code} — {diag.texte}\n"
|
||||
codes_section += f" Justification: {justif}\n"
|
||||
codes_section += f" Preuves: {preuves}\n\n"
|
||||
|
||||
ctx = build_enriched_context(dossier)
|
||||
ctx_str = format_enriched_context(ctx)
|
||||
|
||||
from ..prompts import QC_VALIDATION
|
||||
prompt = QC_VALIDATION.format(ctx_str=ctx_str, codes_section=codes_section)
|
||||
|
||||
try:
|
||||
result = call_ollama(prompt, temperature=0.1, max_tokens=2500, role="qc")
|
||||
except Exception:
|
||||
logger.warning("Erreur lors de l'appel Ollama pour validation QC", exc_info=True)
|
||||
return
|
||||
|
||||
if result is None:
|
||||
return
|
||||
|
||||
# Appliquer les ajustements
|
||||
validations = result.get("validations", [])
|
||||
for v in validations:
|
||||
if not isinstance(v, dict):
|
||||
continue
|
||||
num = v.get("numero")
|
||||
if not isinstance(num, int) or num < 1 or num > len(all_diags):
|
||||
continue
|
||||
type_diag, diag = all_diags[num - 1]
|
||||
conf = v.get("confidence_recommandee")
|
||||
verdict = v.get("verdict")
|
||||
commentaire = v.get("commentaire", "")
|
||||
|
||||
if conf in ("high", "medium", "low") and conf != diag.cim10_confidence:
|
||||
old = diag.cim10_confidence
|
||||
diag.cim10_confidence = conf
|
||||
if old and conf != old:
|
||||
dossier.alertes_codage.append(
|
||||
f"QC: {type_diag} {diag.cim10_suggestion} confiance {old}\u2192{conf} \u2014 {commentaire}"
|
||||
)
|
||||
|
||||
if verdict == "supprimer" and type_diag == "DAS":
|
||||
dossier.alertes_codage.append(
|
||||
f"QC: DAS {diag.cim10_suggestion} ({diag.texte}) à reconsidérer \u2014 {commentaire}"
|
||||
)
|
||||
|
||||
alertes_globales = result.get("alertes_globales", [])
|
||||
if isinstance(alertes_globales, str):
|
||||
alertes_globales = [alertes_globales]
|
||||
for a in alertes_globales:
|
||||
if isinstance(a, str) and a.strip():
|
||||
dossier.alertes_codage.append(f"QC: {a}")
|
||||
|
||||
logger.info(" QC batch : %d validations, %d alertes globales",
|
||||
len(validations), len(alertes_globales))
|
||||
Reference in New Issue
Block a user