File size: 6,481 Bytes
fd20bd2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine
from typing import Dict, List
import re
import logging
logger = logging.getLogger(__name__)
class PIIDetector:
"""Service to detect and remove Personal Identifiable Information from medical notes"""
def __init__(self):
"""Initialize PII detection engines"""
try:
self.analyzer = AnalyzerEngine()
self.anonymizer = AnonymizerEngine()
# Entities to detect (common in medical notes)
self.entities_to_detect = [
"PERSON", # Names
"EMAIL_ADDRESS", # Email
"PHONE_NUMBER", # Phone numbers
"US_SSN", # Social Security Number
"CREDIT_CARD", # Credit card numbers
"US_DRIVER_LICENSE", # Driver's license
"LOCATION", # Addresses, cities
"DATE_TIME", # Birth dates, appointment dates
"US_PASSPORT", # Passport numbers
"MEDICAL_LICENSE", # Medical license numbers
"IP_ADDRESS", # IP addresses
"URL" # URLs
]
logger.info("✅ PII Detector initialized successfully")
except Exception as e:
logger.error(f"❌ Failed to initialize PII Detector: {str(e)}")
raise
def detect_pii(self, text: str) -> List[Dict]:
"""
Detect PII entities in text
Args:
text: Input text to analyze
Returns:
List of detected PII entities with details
"""
try:
results = self.analyzer.analyze(
text=text,
entities=self.entities_to_detect,
language='en'
)
pii_findings = []
for result in results:
pii_findings.append({
"entity_type": result.entity_type,
"start": result.start,
"end": result.end,
"score": result.score,
"text": text[result.start:result.end]
})
logger.info(f"🔍 Detected {len(pii_findings)} PII entities")
return pii_findings
except Exception as e:
logger.error(f"❌ Error detecting PII: {str(e)}")
return []
def remove_pii(self, text: str) -> Dict[str, any]:
"""
Remove PII from text while preserving medical information
Args:
text: Input text containing potential PII
Returns:
Dictionary with sanitized text and PII removal report
"""
try:
# Step 1: Detect PII
analyzer_results = self.analyzer.analyze(
text=text,
entities=self.entities_to_detect,
language='en'
)
if not analyzer_results:
logger.info("✅ No PII detected in text")
return {
"sanitized_text": text,
"pii_detected": [],
"pii_count": 0,
"was_pii_removed": False
}
# Step 2: Anonymize detected PII
anonymized_result = self.anonymizer.anonymize(
text=text,
analyzer_results=analyzer_results
)
sanitized_text = anonymized_result.text
# Step 3: Additional pattern-based cleaning for medical notes
# Replace common medical note PII patterns
sanitized_text = self._clean_medical_patterns(sanitized_text)
# Step 4: Collect PII detection details
pii_detected = []
for result in analyzer_results:
pii_detected.append({
"entity_type": result.entity_type,
"start": result.start,
"end": result.end,
"score": result.score
})
logger.info(f"✅ Removed {len(pii_detected)} PII entities from text")
return {
"sanitized_text": sanitized_text,
"pii_detected": pii_detected,
"pii_count": len(pii_detected),
"was_pii_removed": True
}
except Exception as e:
logger.error(f"❌ Error removing PII: {str(e)}")
# Return original text if PII removal fails
return {
"sanitized_text": text,
"pii_detected": [],
"pii_count": 0,
"was_pii_removed": False,
"error": str(e)
}
def _clean_medical_patterns(self, text: str) -> str:
"""
Clean common medical note PII patterns that might be missed
Args:
text: Text to clean
Returns:
Cleaned text
"""
# Pattern 1: "Patient: <NAME>" or "Pt: <NAME>"
text = re.sub(
r'(Patient|Pt|Patient Name):\s*<[A-Z_]+>',
r'\1: [REDACTED]',
text,
flags=re.IGNORECASE
)
# Pattern 2: "DOB: <DATE>"
text = re.sub(
r'(DOB|Date of Birth|Birth Date):\s*<[A-Z_]+>',
r'\1: [REDACTED]',
text,
flags=re.IGNORECASE
)
# Pattern 3: "Address: <LOCATION>"
text = re.sub(
r'(Address|Addr|Home Address):\s*<[A-Z_]+>',
r'\1: [REDACTED]',
text,
flags=re.IGNORECASE
)
# Pattern 4: "Phone: <PHONE_NUMBER>"
text = re.sub(
r'(Phone|Tel|Telephone|Cell|Mobile):\s*<[A-Z_]+>',
r'\1: [REDACTED]',
text,
flags=re.IGNORECASE
)
# Pattern 5: "MRN: <NUMBER>" (Medical Record Number)
text = re.sub(
r'(MRN|Medical Record Number|Record #):\s*<[A-Z_]+>',
r'\1: [REDACTED]',
text,
flags=re.IGNORECASE
)
return text
# Singleton instance
pii_detector = PIIDetector() |