> ML_RECIPE // CLINICAL-NOTES-NER-DEIDENTIFICATION_v1.0
Clinical Notes NER & HIPAA/KVKK De-identification
Extract medical conditions, dosages, and protect patient privacy by scrubbing names, dates, and locations from unstructured doctor notes with 99.5% entity recall under deterministic human supervision.
Business Outcome
Extract medical conditions, dosages, and protect patient privacy by scrubbing names, dates, and locations from unstructured doctor notes with 99.5% entity recall under deterministic human supervision.
Zero leaked primary patient direct identifiers (names, SSN, TC Kimlik) in 10,000 test note audit.
Heuristic Baseline
Regex pattern matching: Comprehensive regular expressions scanning for SSN, phone numbers, Turkish TC Kimlik, and date formats.
Regex alone achieved only 81% recall on names and failed on misspellings and unstructured narrative sentences.
Phase 1: Prototype Path
Evaluate en_core_web_trf / custom clinical spaCy pipeline on synthetic medical record sample. Measure precision and recall across PHI categories.
Phase 2: Production Path
Package spaCy pipeline into hardened Docker container. Deploy within hospital air-gapped on-premise Kubernetes cluster with CPU inference and strict audit logging.
Compute & Placement Topologies
Fine-tuned on isolated private GPU server with synthetic/de-identified training data
On-premise air-gapped hospital server running CPU inference under private intranet
3-Plan Placement Alternatives
Local Python CLI script running rule-based regex + small spaCy model on doctor desktop.
Multi-core on-premise hospital application server running quantized transformer pipeline on CPU.
Air-gapped on-premise Kubernetes deployment with mutual TLS + audit trail logging + deterministic human review queue for low-confidence entity masks.
Recommended Libraries & Tools
Governance, Safeguards & Risks
- Safety & regulatory boundary: This system is strictly an administrative and research assistance tool for text de-identification; it MUST NOT be used as an autonomous clinical diagnostic or medical decision-making system.
- Human-in-the-loop oversight: All entity redactions for research release must pass a deterministic human verification queue for confidence scores below 0.99.
- Hospital data governance mandate: Strictly forbid external cloud API transmission of unmasked patient records.
- Model supply chain: Require SafeTensors or signed ONNX model packaging to eliminate arbitrary code execution inside the hospital clinical network.