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OmniDiag — NLP Clinical Notes Parser
======================================
Extracts structured clinical features from free-text clinical notes using
a two-tier approach:
1. Primary: BioBERT / ClinicalBERT NER via HuggingFace Transformers
(loaded lazily — first call initialises the pipeline).
2. Fallback: Rule-based regex patterns (runs offline, zero dependencies).
The output is a dict suitable for passing directly to the prediction API,
pre-filled with whatever values could be extracted from the note.
Missing values are omitted so the frontend can prompt the user to fill them in.
Supported diseases: heart_disease, diabetes
"""
import re
import logging
import os
from typing import Any, Dict, Optional
log = logging.getLogger("omnidiag.nlp")
# ---------------------------------------------------------------------------
# Regex patterns for the rule-based fallback
# ---------------------------------------------------------------------------
_PATTERNS: Dict[str, list] = {
"age": [
r"\b(\d{1,3})[- ]?(?:year[s]?[- ]?old|y/?o|yr[s]?)\b",
r"\bage[:\s]+(\d{1,3})\b",
# "45 male" / "45-year-old female" / "Patient: 45, female"
r"\b(\d{2,3})\s*[-,]?\s*(?:year[s]?[-\s]?old\s+)?(?:male|female|man|woman)\b",
],
"sex_male": [r"\b(male|man|he|his|gentleman|boy)\b"],
"sex_female": [r"\b(female|woman|she|her|lady|girl)\b"],
"bp_systolic": [
r"(?:bp|blood pressure)[:\s]*(\d{2,3})\s*/\s*\d{2,3}",
r"(?:systolic|sbp)[:\s]*(\d{2,3})",
# Standalone: "BP 140" or "BP: 140" without diastolic
r"(?:bp|blood pressure)[:\s]+(\d{2,3})\b",
],
"bp_diastolic": [
r"(?:bp|blood pressure)[:\s]*\d{2,3}\s*/\s*(\d{2,3})",
r"(?:diastolic|dbp)[:\s]*(\d{2,3})",
],
"cholesterol": [
r"(?:cholesterol|ldl|hdl|total chol)[:\s]*(\d{2,3})\s*(?:mg/dl|mg)?",
],
"glucose": [
r"(?:glucose|blood sugar|fbs|rbs|bgr)[:\s]*(\d{2,3})\s*(?:mg/dl|mg)?",
],
"bmi": [
r"(?:bmi|body mass index)[:\s]*(\d{1,2}(?:\.\d)?)",
],
"heart_rate": [
r"(?:hr|heart rate|pulse)[:\s]*(\d{2,3})\s*(?:bpm)?",
],
"creatinine": [
r"(?:creatinine|cr|scr)[:\s]*(\d+(?:\.\d+)?)\s*(?:mg/dl|mg)?",
],
"hemoglobin": [
r"(?:hemoglobin|hgb|hb)[:\s]*(\d+(?:\.\d+)?)\s*(?:g/dl|gms?)?",
],
"smoking": [
r"\b(smok(?:er|ing|ed)|smoker|cigarette|tobacco)\b",
],
"hypertension": [
r"\b(hypertension|htn|high blood pressure)\b",
],
"diabetes": [
r"\b(diabetes|diabetic|dm|t2dm|t1dm)\b",
],
"heart_disease_history": [
r"\b(heart disease|cad|coronary artery disease|mi|myocardial infarction|chd)\b",
],
"stroke_history": [
r"\b(stroke|cva|tia|cerebrovascular)\b",
],
"chest_pain": [
r"\b(chest pain|angina|ata|typical angina|atypical angina)\b",
],
"exercise_angina": [
r"\b(exercise.?induced angina|angina on exertion|exertional angina)\b",
],
"oldpeak": [
r"(?:st depression|oldpeak|st.?segment)[:\s]*(\d+(?:\.\d+)?)",
],
"marriage": [r"\b(married|spouse|husband|wife)\b"],
"edema": [r"\b(edema|oedema|swelling|pedal edema)\b"],
"appetite": [r"\b(poor appetite|anorexia|not eating|reduced appetite)\b"],
"anemia": [r"\b(anemia|anaemia|low haemoglobin|iron deficiency)\b"],
}
def _regex_extract(text: str) -> Dict[str, Any]:
text_lower = text.lower()
extracted: Dict[str, Any] = {}
def first_match(patterns):
for pat in patterns:
m = re.search(pat, text_lower, re.IGNORECASE)
if m:
return m
return None
# Numeric extractions
for key in ("age", "bp_systolic", "bp_diastolic", "cholesterol", "glucose",
"bmi", "heart_rate", "creatinine", "hemoglobin", "oldpeak"):
m = first_match(_PATTERNS[key])
if m:
try:
extracted[key] = float(m.group(1))
except (IndexError, ValueError):
pass
# Boolean / categorical extractions
if first_match(_PATTERNS["sex_male"]):
extracted["sex"] = "Male"
elif first_match(_PATTERNS["sex_female"]):
extracted["sex"] = "Female"
extracted["hypertension"] = 1 if first_match(_PATTERNS["hypertension"]) else None
extracted["diabetes_flag"] = 1 if first_match(_PATTERNS["diabetes"]) else None
extracted["heart_disease_flag"] = 1 if first_match(_PATTERNS["heart_disease_history"]) else None
extracted["stroke_flag"] = 1 if first_match(_PATTERNS["stroke_history"]) else None
extracted["smoking_flag"] = 1 if first_match(_PATTERNS["smoking"]) else None
extracted["chest_pain_flag"] = 1 if first_match(_PATTERNS["chest_pain"]) else None
extracted["exercise_angina"] = "Y" if first_match(_PATTERNS["exercise_angina"]) else None
extracted["ever_married"] = "Yes" if first_match(_PATTERNS["marriage"]) else None
extracted["edema_flag"] = 1 if first_match(_PATTERNS["edema"]) else None
extracted["poor_appetite"] = 1 if first_match(_PATTERNS["appetite"]) else None
extracted["anemia_flag"] = 1 if first_match(_PATTERNS["anemia"]) else None
# Remove None values
return {k: v for k, v in extracted.items() if v is not None}
# ---------------------------------------------------------------------------
# BioBERT / ClinicalBERT NER (lazy-loaded)
# ---------------------------------------------------------------------------
_ner_pipeline = None
_NER_MODEL = os.getenv("CLINICAL_NER_MODEL", "d4data/biomedical-ner-all")
def _get_ner_pipeline():
global _ner_pipeline
if _ner_pipeline is None:
try:
from transformers import pipeline # type: ignore
log.info(f"Loading clinical NER model: {_NER_MODEL}")
_ner_pipeline = pipeline(
"ner",
model=_NER_MODEL,
aggregation_strategy="simple",
device=-1, # CPU
)
log.info("Clinical NER pipeline ready")
except Exception as exc:
log.warning(f"Failed to load NER model ({exc!r}). Falling back to regex.")
_ner_pipeline = "unavailable"
return _ner_pipeline if _ner_pipeline != "unavailable" else None
def _bert_extract(text: str) -> Dict[str, Any]:
pipe = _get_ner_pipeline()
if pipe is None:
return {}
try:
entities = pipe(text)
extracted: Dict[str, Any] = {}
for ent in entities:
label = ent.get("entity_group", "").upper()
word = ent.get("word", "").strip()
score = ent.get("score", 0.0)
if score < 0.7:
continue
if label in ("AGE",):
m = re.search(r"\d+", word)
if m:
extracted["age"] = float(m.group())
elif label in ("DISEASE", "CONDITION", "PROBLEM"):
word_lower = word.lower()
if any(x in word_lower for x in ("hypertension", "htn")):
extracted["hypertension"] = 1
if any(x in word_lower for x in ("diabetes", "dm")):
extracted["diabetes_flag"] = 1
if any(x in word_lower for x in ("stroke", "cva")):
extracted["stroke_flag"] = 1
if any(x in word_lower for x in ("heart disease", "cad")):
extracted["heart_disease_flag"] = 1
if any(x in word_lower for x in ("anemia", "anaemia")):
extracted["anemia_flag"] = 1
return extracted
except Exception as exc:
log.warning(f"NER extraction error: {exc!r}")
return {}
# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------
# ---------------------------------------------------------------------------
# Disease-specific field mappers
# Maps generic extracted keys → schema field names for each disease
# ---------------------------------------------------------------------------
_HEART_DISEASE_MAP = {
"age": ("Age", lambda v: int(v)),
"sex": ("Sex", lambda v: "M" if str(v).lower().startswith("m") else "F"),
"bp_systolic": ("RestingBP", lambda v: int(v)),
"cholesterol": ("Cholesterol", lambda v: int(v)),
"heart_rate": ("MaxHR", lambda v: int(v)),
"oldpeak": ("Oldpeak", lambda v: float(v)),
"hypertension": ("FastingBS", lambda v: 1),
"chest_pain_flag": ("ChestPainType", lambda v: "ASY"),
"exercise_angina": ("ExerciseAngina", lambda v: "Y"),
}
_DIABETES_MAP = {
"age": ("Age", lambda v: max(1, min(13, round(int(v) / 7)))),
"bmi": ("BMI", lambda v: float(v)),
"bp_systolic": ("HighBP", lambda v: 1 if int(v) >= 130 else 0),
"cholesterol": ("HighChol", lambda v: 1 if int(v) >= 200 else 0),
"sex": ("Sex", lambda v: 1 if str(v).lower().startswith("m") else 0),
"hypertension": ("HighBP", lambda v: 1),
"heart_disease_flag": ("HeartDiseaseorAttack",lambda v: 1),
"stroke_flag": ("Stroke", lambda v: 1),
"smoking_flag": ("Smoker", lambda v: 1),
}
def map_to_disease_schema(extracted: Dict[str, Any], disease: str) -> Dict[str, Any]:
"""Map generic NLP-extracted fields to disease-specific schema field names."""
mapping = {"heart_disease": _HEART_DISEASE_MAP, "diabetes": _DIABETES_MAP}.get(disease, {})
result: Dict[str, Any] = {}
for generic_key, (schema_key, transform) in mapping.items():
if generic_key in extracted:
try:
result[schema_key] = transform(extracted[generic_key])
except Exception:
pass
return result
def parse_clinical_note(note: str, use_bert: bool = True) -> Dict[str, Any]:
"""
Parse a free-text clinical note and extract structured features.
Args:
note: The clinical note text.
use_bert: Whether to attempt BioBERT NER (falls back to regex on failure).
Returns:
Dict of extracted feature_name → value. Only present for detected values.
Numeric values are Python floats; categorical values are strings.
"""
if not note or not note.strip():
return {}
# Regex baseline (always runs)
result = _regex_extract(note)
# Merge BERT results (BERT takes precedence for overlapping keys)
if use_bert:
bert_result = _bert_extract(note)
result.update(bert_result)
return result
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