import logging
import re
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
class MedicalReportAnalyzer:
"""
A class for analyzing medical text reports using pre-trained NLP models from Hugging Face.
This analyzer can:
1. Extract medical entities (conditions, treatments, tests)
2. Classify report severity
3. Extract key findings
4. Identify suggested follow-up actions
"""
def __init__(
self,
ner_model="samrawal/bert-base-uncased_medical-ner",
classifier_model="medicalai/ClinicalBERT",
device=None,
):
"""
Initialize the text analyzer with specific pre-trained models.
Args:
ner_model (str): Model for named entity recognition
classifier_model (str): Model for text classification
device (str, optional): Device to run models on ('cuda' or 'cpu')
"""
self.logger = logging.getLogger(__name__)
# Determine device
if device is None:
self.device = "cuda" if torch.cuda.is_available() else "cpu"
else:
self.device = device
self.logger.info(f"Using device: {self.device}")
# Load NER model for entity extraction
try:
self.ner_pipeline = pipeline(
"token-classification",
model=ner_model,
aggregation_strategy="simple",
device=0 if self.device == "cuda" else -1,
)
self.logger.info(f"Successfully loaded NER model: {ner_model}")
except Exception as e:
self.logger.error(f"Failed to load NER model: {e}")
self.ner_pipeline = None
# Load classifier model for severity assessment
try:
self.tokenizer = AutoTokenizer.from_pretrained(classifier_model)
self.classifier = AutoModelForSequenceClassification.from_pretrained(
classifier_model
)
self.classifier.to(self.device)
self.classifier.eval()
self.logger.info(
f"Successfully loaded classifier model: {classifier_model}"
)
except Exception as e:
self.logger.error(f"Failed to load classifier model: {e}")
self.classifier = None
# Severity levels mapping
self.severity_levels = {
0: "Normal",
1: "Mild",
2: "Moderate",
3: "Severe",
4: "Critical",
}
# Common medical findings and their severity levels
self.finding_severity = {
"pneumonia": 3,
"fracture": 3,
"tumor": 4,
"nodule": 2,
"mass": 3,
"edema": 2,
"effusion": 2,
"hemorrhage": 3,
"opacity": 1,
"atelectasis": 2,
"pneumothorax": 3,
"consolidation": 2,
"cardiomegaly": 2,
}
def extract_entities(self, text):
"""
Extract medical entities from the report text.
Args:
text (str): Medical report text
Returns:
dict: Dictionary of entity lists by category
"""
if not self.ner_pipeline:
self.logger.warning("NER model not available")
return {}
try:
# Run NER
entities = self.ner_pipeline(text)
# Group entities by type
grouped_entities = {
"problem": [], # Medical conditions
"test": [], # Tests/procedures
"treatment": [], # Treatments/medications
"anatomy": [], # Anatomical locations
}
for entity in entities:
entity_type = entity.get("entity_group", "").lower()
# Map entity types to our categories
if entity_type in ["problem", "disease", "condition", "diagnosis"]:
category = "problem"
elif entity_type in ["test", "procedure", "examination"]:
category = "test"
elif entity_type in ["treatment", "medication", "drug"]:
category = "treatment"
elif entity_type in ["body_part", "anatomy", "organ"]:
category = "anatomy"
else:
continue # Skip other entity types
word = entity.get("word", "")
score = entity.get("score", 0)
# Only include if confidence is reasonable
if score > 0.7 and word not in grouped_entities[category]:
grouped_entities[category].append(word)
return grouped_entities
except Exception as e:
self.logger.error(f"Error extracting entities: {e}")
return {}
def assess_severity(self, text):
"""
Assess the severity level of the medical report.
Args:
text (str): Medical report text
Returns:
dict: Severity assessment including level and confidence
"""
if not self.classifier:
self.logger.warning("Classifier model not available")
return {"level": "Unknown", "score": 0.0}
try:
# Use rule-based approach along with model
severity_score = 0
confidence = 0.5 # Start with neutral confidence
# Check for severe keywords
severe_keywords = [
"severe",
"critical",
"urgent",
"emergency",
"immediate attention",
]
moderate_keywords = ["moderate", "concerning", "follow-up", "monitor"]
mild_keywords = ["mild", "minimal", "slight", "minor"]
normal_keywords = [
"normal",
"unremarkable",
"no abnormalities",
"within normal limits",
]
# Count keyword occurrences
text_lower = text.lower()
severe_count = sum(text_lower.count(word) for word in severe_keywords)
moderate_count = sum(text_lower.count(word) for word in moderate_keywords)
mild_count = sum(text_lower.count(word) for word in mild_keywords)
normal_count = sum(text_lower.count(word) for word in normal_keywords)
# Adjust severity based on keyword counts
if severe_count > 0:
severity_score += min(severe_count, 2) * 1.5
confidence += 0.1
if moderate_count > 0:
severity_score += min(moderate_count, 3) * 0.75
confidence += 0.05
if mild_count > 0:
severity_score += min(mild_count, 3) * 0.25
confidence += 0.05
if normal_count > 0:
severity_score -= min(normal_count, 3) * 0.75
confidence += 0.1
# Check for specific medical findings
for finding, level in self.finding_severity.items():
if finding in text_lower:
severity_score += level * 0.5
confidence += 0.05
# Normalize severity score to 0-4 range
severity_score = max(0, min(4, severity_score))
severity_level = int(round(severity_score))
# Map to severity level
severity = self.severity_levels.get(severity_level, "Moderate")
# Cap confidence at 0.95
confidence = min(0.95, confidence)
return {
"level": severity,
"score": round(severity_score, 1),
"confidence": round(confidence, 2),
}
except Exception as e:
self.logger.error(f"Error assessing severity: {e}")
return {"level": "Unknown", "score": 0.0, "confidence": 0.0}
def extract_findings(self, text):
"""
Extract key clinical findings from the report.
Args:
text (str): Medical report text
Returns:
list: List of key findings
"""
try:
# Split text into sentences
sentences = re.split(r"[.!?]\s+", text)
findings = []
# Key phrases that often introduce findings
finding_markers = [
"finding",
"observed",
"noted",
"shows",
"reveals",
"demonstrates",
"indicates",
"evident",
"apparent",
"consistent with",
"suggestive of",
]
# Negative markers
negation_markers = ["no", "not", "none", "negative", "without", "denies"]
for sentence in sentences:
# Skip very short sentences
if len(sentence.split()) < 3:
continue
sentence = sentence.strip()
# Check if this sentence likely contains a finding
contains_finding_marker = any(
marker in sentence.lower() for marker in finding_markers
)
# Check for negation
contains_negation = any(
marker in sentence.lower().split() for marker in negation_markers
)
# Only include positive findings or explicitly negated findings that are important
if contains_finding_marker or (
contains_negation
and any(
term in sentence.lower()
for term in self.finding_severity.keys()
)
):
findings.append(sentence)
return findings
except Exception as e:
self.logger.error(f"Error extracting findings: {e}")
return []
def suggest_followup(self, text, entities, severity):
"""
Suggest follow-up actions based on report analysis.
Args:
text (str): Medical report text
entities (dict): Extracted entities
severity (dict): Severity assessment
Returns:
list: Suggested follow-up actions
"""
try:
followups = []
# Base recommendations on severity
severity_level = severity.get("level", "Unknown")
severity_score = severity.get("score", 0)
# Extract problems from entities
problems = entities.get("problem", [])
# Check if follow-up is already mentioned in the text
followup_mentioned = any(
phrase in text.lower()
for phrase in [
"follow up",
"follow-up",
"followup",
"return",
"refer",
"consult",
]
)
# Default recommendations based on severity
if severity_level == "Critical":
followups.append("Immediate specialist consultation recommended.")
elif severity_level == "Severe":
followups.append("Prompt follow-up with specialist is recommended.")
# Add specific recommendations for common severe conditions
for problem in problems:
if "pneumonia" in problem.lower():
followups.append(
"Consider antibiotic therapy and close monitoring."
)
elif "fracture" in problem.lower():
followups.append(
"Orthopedic consultation for treatment planning."
)
elif "mass" in problem.lower() or "tumor" in problem.lower():
followups.append(
"Further imaging and possible biopsy recommended."
)
elif severity_level == "Moderate":
followups.append("Follow-up with primary care physician recommended.")
if not followup_mentioned and problems:
followups.append(
"Consider additional imaging or tests for further evaluation."
)
elif severity_level == "Mild":
if problems:
followups.append(
"Routine follow-up with primary care physician as needed."
)
else:
followups.append("No immediate follow-up required.")
else: # Normal
followups.append(
"No specific follow-up indicated based on this report."
)
# Check for specific findings that always need follow-up
for critical_term in ["mass", "tumor", "nodule", "opacity"]:
if (
critical_term in text.lower()
and "follow-up" not in " ".join(followups).lower()
):
followups.append(
f"Follow-up imaging recommended to monitor {critical_term}."
)
break
return followups
except Exception as e:
self.logger.error(f"Error suggesting follow-up: {e}")
return ["Unable to generate follow-up recommendations."]
def analyze(self, text):
"""
Perform comprehensive analysis of medical report text.
Args:
text (str): Medical report text
Returns:
dict: Complete analysis results
"""
try:
# Extract entities
entities = self.extract_entities(text)
# Assess severity
severity = self.assess_severity(text)
# Extract key findings
findings = self.extract_findings(text)
# Generate follow-up suggestions
followups = self.suggest_followup(text, entities, severity)
# Create detailed report
report = {
"entities": entities,
"severity": severity,
"findings": findings,
"followup_recommendations": followups,
}
return report
except Exception as e:
self.logger.error(f"Error analyzing report: {e}")
return {"error": str(e)}
# Example usage
if __name__ == "__main__":
# Set up logging
logging.basicConfig(level=logging.INFO)
# Test on a sample report
analyzer = MedicalReportAnalyzer()
sample_report = """
CHEST X-RAY EXAMINATION
CLINICAL HISTORY: 55-year-old male with cough and fever.
FINDINGS: The heart size is at the upper limits of normal. The lungs are clear without focal consolidation,
effusion, or pneumothorax. There is mild prominence of the pulmonary vasculature. No pleural effusion is seen.
There is a small nodular opacity noted in the right lower lobe measuring approximately 8mm, which is suspicious
and warrants further investigation. The mediastinum is unremarkable. The visualized bony structures show no acute abnormalities.
IMPRESSION:
1. Mild cardiomegaly.
2. 8mm nodular opacity in the right lower lobe, recommend follow-up CT for further evaluation.
3. No acute pulmonary parenchymal abnormality.
RECOMMENDATIONS: Follow-up chest CT to further characterize the nodular opacity in the right lower lobe.
"""
results = analyzer.analyze(sample_report)
print("\nMedical Report Analysis:")
print(
f"\nSeverity: {results['severity']['level']} (Score: {results['severity']['score']})"
)
print("\nKey Findings:")
for finding in results["findings"]:
print(f"- {finding}")
print("\nEntities:")
for category, items in results["entities"].items():
if items:
print(f"- {category.capitalize()}: {', '.join(items)}")
print("\nFollow-up Recommendations:")
for rec in results["followup_recommendations"]:
print(f"- {rec}")