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}")